Detecting, quantifying, and/or classifying seizures using multimodal data

ABSTRACT

Methods, systems, and apparatus for detecting an epileptic event, for example, a seizure in a patient using a medical device. The determination is performed by providing an autonomic signal indicative of the patient&#39;s autonomic activity; providing a neurologic signal indicative of the patient&#39;s neurological activity; and detecting an epileptic event based upon the autonomic signal and the neurologic signal.

FIELD OF THE INVENTION

This invention relates to medical device systems and methods capable ofdetecting and, in some embodiments, treating an occurring or impendingseizure using multimodal data.

DESCRIPTION OF THE RELATED ART

Of the approximately 60 million people worldwide affected with epilepsy,roughly 23 million people suffer from epilepsy resistant to multiplemedications. In the USA alone, the annual cost of epilepsy care is USD12 billion (in 1995 dollars), most of which is attributable to subjectswith pharmaco-resistant seizures. Pharmaco-resistant seizures areassociated with an increase mortality and morbidity (e.g., compared tothe general population and to epileptics whose seizures are controlledby medications) and with markedly degraded quality of life for patients.Seizures may impair motor control, responsiveness to a wide class ofstimuli, and other cognitive functions. The sudden onset of a patient'simpairment of motor control, responsiveness, and other cognitivefunctions precludes the performance of necessary and even simple dailylife tasks such as driving a vehicle, cooking, or operating machinery,as well as more complex tasks such as acquiring knowledge andsocializing.

Therapies using electrical currents or fields to provide a therapy to apatient (electrotherapy) are beneficial for certain neurologicaldisorders, such as epilepsy. Implantable medical devices have beeneffectively used to deliver therapeutic electrical stimulation tovarious portions of the human body (e.g., the vagus nerve) for treatingepilepsy. As used herein, “stimulation,” “neurostimulation,”“stimulation signal,” “therapeutic signal,” or “neurostimulation signal”refers to the direct or indirect application of an electrical,mechanical, magnetic, electro-magnetic, photonic, acoustic, cognitive,and/or chemical signal to an organ or a neural structure in thepatient's body. The signal is an exogenous signal that is distinct fromthe endogenous electro-chemical activity inherent to the patient's bodyand also from that found in the environment. In other words, thestimulation signal (whether electrical, mechanical, magnetic,electro-magnetic, photonic, acoustic, cognitive, and/or chemical innature) applied to a cranial nerve or to other nervous tissue structurein the present invention is a signal applied from a medical device,e.g., a neurostimulator.

A “therapeutic signal” refers to a stimulation signal delivered to apatient's body with the intent of treating a medical condition through asuppressing (e.g., blocking) or modulating effect to neural tissue. Theeffect of a stimulation signal on neuronal activity may be suppressingor modulating; however, for simplicity, the terms “stimulating”,suppressing, and modulating, and variants thereof, are sometimes usedinterchangeably herein. In general, however, the delivery of anexogenous signal itself refers to “stimulation” of an organ or a neuralstructure, while the effects of that signal, if any, on the electricalactivity of the neural structure are properly referred to as suppressionor modulation.

Depending upon myriad factors such as the history (recent and distant)of the nervous system, stimulation parameters and time of day, to name afew, the effects of stimulation upon the neural tissue may be excitatoryor inhibitory, facilitatory or disfacilitatory and may suppress,enhance, or leave unaltered neuronal activity. For example, thesuppressing effect of a stimulation signal on neural tissue wouldmanifest as the blockage of abnormal activity (e.g., epileptic seizures)see Osorio et al., Ann Neurol 2005; Osorio & Frei IJNS 2009) Themechanisms thorough which this suppressing effect takes place aredescribed in the foregoing articles. Suppression of abnormal neuralactivity is generally a threshold or suprathreshold process and thetemporal scale over which it occurs is usually in the order of tens orhundreds of milliseconds. Modulation of abnormal or undesirable neuralactivity is typically a “sub-threshold” process in the spatio-temporaldomain that may summate and result under certain conditions, inthreshold or suprathreshold neural events. The temporal scale ofmodulation is usually longer than that of suppression, encompassingseconds to hours, even months. In addition to inhibition ordysfacilitation, modification of neural activity (e.g., waveannihilation) may be exerted through collision with identical, similaror dissimilar waves, a concept borrowed from wave mechanics, or throughphase resetting (Winfree).

In some cases, electrotherapy may be provided by implanting anelectrical device, i.e., an implantable medical device (IMD), inside apatient's body for stimulation of a nervous tissue, such as a cranialnerve. Generally, electrotherapy signals that suppress or modulateneural activity are delivered by the IMD via one or more leads. Whenapplicable, the leads generally terminate at their distal ends in one ormore electrodes, and the electrodes, in turn, are coupled to a targettissue in the patient's body. For example, a number of electrodes may beattached to various points of a nerve or other tissue inside a humanbody for delivery of a neurostimulation signal.

While contingent (also referred to as “closed-loop,” “active,” or“feedback” stimulation (i.e., electrotherapy applied in response tosensed information, such as heart rate) stimulation schemes have beenproposed, non-contingent, programmed periodic stimulation is theprevailing modality. For example, vagus nerve stimulation for thetreatment of epilepsy usually involves a series of grouped electricalpulses defined by an “on-time” (such as 30 sec.) and an “off-time” (suchas 5 min.). This type of stimulation is also referred to as “open-loop,”“passive,” or “non-feedback” stimulation. Each sequence of pulses duringan on-time may be referred to as a “pulse burst.” The burst is followedby the off-time period in which no signals are applied to the nerve.During the on-time, electrical pulses of a defined electrical current(e.g., 0.5-3.5 milliamps) and pulse width (e.g., 0.25-1.0 milliseconds)are delivered at a defined frequency (e.g., 20-30 Hz) for a certainduration (e.g., 10-60 seconds). The on-time and off-time parameterstogether define a duty cycle, which is the ratio of the on-time to thesum of the on-time and off-time, and which describes the fraction oftime that the electrical signal is applied to the nerve.

In VNS, the on-time and off-time may be programmed to define anintermittent pattern in which a repeating series of electrical pulsebursts are generated and applied to a cranial nerve such as the vagusnerve. The off-time is provided to minimize adverse effects and conservepower. If the off-time is set at zero, the electrical signal inconventional VNS may provide continuous stimulation to the vagus nerve.Alternatively, the off time may be as long as one day or more, in whichcase the pulse bursts are provided only once per day or at even longerintervals. Typically, however, the ratio of “off-time” to “on-time” mayrange from about 0.5 to about 10.

In addition to the on-time and off-time, the other parameters definingthe electrical signal in VNS may be programmed over a range of values.The pulse width for the pulses in a pulse burst of conventional VNS maybe set to a value not greater than about 1 msec, such as about 250-500μsec, and the number of pulses in a pulse burst is typically set byprogramming a frequency in a range of about 20-300 Hz (i.e., 20 pulsesper second to 300 pulses per second). A non-uniform frequency may alsobe used. Frequency may be altered during a pulse burst by either afrequency sweep from a low frequency to a high frequency, or vice versa.Alternatively, the timing between adjacent individual signals within aburst may be randomly changed such that two adjacent signals may begenerated at any frequency within a range of frequencies.

Although neurostimulation has proven effective in the treatment of anumber of medical conditions, it would be desirable to further enhanceand optimize neurostimulation-based therapy for this purpose. Forexample, it may be desirable to detect an occurring or impendingseizure. Such detection may be useful in triggering a therapy,monitoring the course of a patient's disease, or the progress of his orher treatment thereof. Alternatively or in addition, such detection maybe useful in warning the patient of an impending seizure or alerting thepatient, a physician, a caregiver, or a suitably programmed computer inorder for that person or computer program to take action intended toreduce the likelihood, duration, or severity of the seizure or impendingseizure, or to facilitate further medical treatment or intervention forthe patient. In particular, detection of an occurring or impendingseizure enables the use of contingent neurostimulation. The state of theart does not provide an efficient and effective means for performingsuch detection and/or warning. Conventional VNS stimulation as describedabove does not detect occurring or impending seizures.

Closed-loop neurostimulation therapies for treating epilepsy have beenproposed in which stimulation is triggered based upon factors includingEEG activity (see, e.g., U.S. Pat. No. 5,995,868 and U.S. Pat. No.7,280,867) as well as cardiac-based activity (see., e.g., U.S. Pat. No.6,961,618 and U.S. Pat. No. 5,928,272). EEG- or ECoG-based approachesinvolving recording of neural electrical activity at any spatio-temporalscale involve determination of one or more parameters from brainelectrical activity that indicate a seizure. Such approaches have metwith limited success and have a number of drawbacks, including highlyinvasive and technically demanding and costly surgery for implantedsystems, and the poor patient compliance for external systems (whichrequire the patient to wear electrodes on the scalp for extendedperiods).

SUMMARY OF THE INVENTION

In one embodiment, the present invention provides a method for detectingan epileptic event based upon multimodal signals such as an autonomicsignal and a neurologic signal of a patient. In one embodiment, themethod comprises providing an autonomic signal indicative of thepatient's autonomic activity; providing a neurologic signal indicativeof the patient's neurological activity; and detecting an epileptic eventbased upon the autonomic signal and the neurologic signal.

In one embodiment the autonomic signal is a cardiac signal, and theneurologic signal is a kinetic signal. In another embodiment, a firstautonomic signal is a respiratory signal and a second autonomic signalis a dermal signal (e.g., sweat glands). In yet another embodiment, theautonomic signal is a cardiac signal, the neurologic signal is included,and a metabolic signal is included.

In one embodiment, the present invention provides a computer readableprogram storage device encoded with instructions that, when executed bya computer, performs a method for detecting an epileptic event basedupon a patient's cardiac signal and kinetic activity. The methodincludes: providing a kinetic signal indicative of a body movement ofthe patient; calculating based on the kinetic signal a kinetic scoreindicative of a correlation of said kinetic signal with an epilepticevent; detecting an epileptic event based upon the patient's heart beatsequence; and providing an output indicative of an epileptic event basedon the kinetic score.

In another embodiment, the present invention provides a computerreadable program storage device encoded with instructions that, whenexecuted by a computer, performs a method for detecting an epilepticevent based upon a patient's cardiac signal and kinetic activity. Themethod includes: providing a kinetic signal indicative of a bodymovement of the patient; classifying the kinetic signal as either anepileptic event kinetic signal or a nonepileptic event kinetic signal;detecting an epileptic event based upon changes in the patient's heartbeat sequence; confirming the detecting if said kinetic signal isclassified as an epileptic event kinetic signal; overriding thedetecting if said kinetic signal is classified as a nonepileptic eventkinetic signal; and providing an output indicative of an epileptic eventonly if the detecting is confirmed.

In yet another embodiment, the present invention provides an implantablemedical device for detecting an epileptic event based upon an autonomicsignal and a neurologic signal of a patient. The implantable medicaldevice includes a detection module for receiving an autonomic signalindicative of the patient's autonomic activity and for receiving aneurologic signal indicative of the patient's neurological activity. Theimplantable medical device also includes a processing element fordetermining whether an epileptic event has occurred based upon theautonomic signal and the neurologic signal.

In one embodiment, the present invention provides a method for detectinga primarily or secondarily generalized tonic-clonic seizure based upontwo or more of a patient's body signals. In one embodiment, the methodcomprises providing at least two body signals selected from the groupconsisting of a cardiac signal indicative of the patient's heart beats;an accelerometer signal indicative of the patient's movement; aninclinometer signal indicative of the patient's body position; anactigraph signal indicative of the patient's movement, body position, orboth; a respiratory signal indicative of the patient's respiration; askin resistivity signal indicative of the patient's skin resistivity; anblood gas signal indicative of the patient's blood oxygen content,carbon dioxide content, or both; a blood pH signal indicative of thepatient's blood pH; an isometric force signal indicative of thepatient's muscle activity; a sound signal indicative of the patient'soral utterances or vocalizations; an ocular signal indicative of thepatient's eye movement; a responsiveness signal indicative of thepatient's responsiveness; an awareness signal indicative of thepatient's awareness; and a stress marker signal indicative of at leastone stress marker of the patient; and detecting the generalizedtonic-clonic epileptic seizure based upon the timewise correlation oftwo features, one feature being of each of the at least two bodysignals, wherein the feature of the cardiac signal is an increase in thepatient's heart rate above a reference value; the feature of theaccelerometer signal is an increase in the patient's movement above areference value followed by a decrease in the patient's movement below areference value; the feature of the inclinometer signal is a change ofthe patient's body position indicative of a fall; the feature of therespiratory signal is a respiration rate outside of an interictalreference range; the feature of the skin resistivity signal is adecrease in the patient's skin resistivity below a reference value; thefeature of the blood gas signal is a decrease in the patient's bloodoxygen content below a reference value, an increase in carbon dioxidecontent above a reference value, or both; the feature of the blood pHsignal is a decrease in the patient's blood pH below a reference value;the feature of the isometric force signal is an increase in thepatient's muscle activity above a reference value; the feature of thesound signal is an increase in the patient's oral utterances orvocalizations indicative of an epileptic cry; the feature of the ocularsignal is an increase in the patient's eye movement above a referencevalue; the feature of the responsiveness signal is a decrease in thepatient's responsiveness below a reference value; the feature of theawareness signal is a decrease in the patient's awareness below areference value; and the feature of the stress marker signal is anincrease in at least one stress marker of the patient above a referencevalue.

In one embodiment, the present invention also provides a computerreadable program storage device encoded with instructions that, whenexecuted by a computer, performs a method for detecting a partialepileptic seizure based upon two or more of a patient's body signals. Inone embodiment, this method comprises providing at least two bodysignals selected from the group consisting of a cardiac signalindicative of the patient's heart beats; an accelerometer signalindicative of the patient's movement; an inclinometer signal indicativeof the patient's body position; an actigraph signal indicative of thepatient's movement, body position, or both; a respiratory signalindicative of the patient's respiration; a skin resistivity signalindicative of the patient's skin resistivity; an blood gas signalindicative of the patient's blood oxygen content, carbon dioxidecontent, or both; a blood pH signal indicative of the patient's bloodpH; a sound signal indicative of the patient's oral utterances orvocalizations; a responsiveness signal indicative of the patient'sresponsiveness; an awareness signal indicative of the patient'sawareness; and a stress marker signal indicative of at least one stressmarker of the patient; and detecting the partial epileptic seizure basedupon the timewise correlation of two features, one feature being of eachof the at least two body signals, wherein the feature of the cardiacsignal is a heart rate outside an interictal reference value range; thefeature of the accelerometer signal is a movement velocity outside aninterictal reference value range; the feature of the respiratory signalis a respiration rate, tidal volume, minute volume, or pattern outsideof an interictal reference value range; the feature of the skinresistivity signal is a skin resistivity outside an interictal referencevalue range; the feature of the blood gas signal is an increase in thepatient's blood oxygen content above an interictal reference value, adecrease in carbon dioxide content below an interictal reference value,or both; the feature of the blood pH signal is a blood pH outside aninterictal reference value range; the feature of the sound signal is achange in the pattern, loudness, timbre or quality, semantic content andcontextual relevance of the patient's oral utterances or vocalizationscompared to utterances or vocalizations to the non-seizure state; andthe feature of the stress marker signal is a concentration of at leastone stress marker outside an interictal reference value range.

In yet another aspect of the present invention, a computer readableprogram storage device is provided that is encoded with instructionsthat, when executed by a computer, perform a method described above.

In one embodiment, a medical device is provided comprising an autonomicsignal module, a kinetic signal module, a detection module, and aprocessor adapted to perform a method as described above.

In yet another aspect of the present invention, a medical device systemfor detecting an epileptic event based upon multimodal signals, isprovided. The medical device system includes a sensor for detecting afirst modal data and a second modal data relating to a patient's body.The first modal data includes an autonomic signal and the second modaldata includes a neurologic signal of the patient's body. The medicaldevice system also includes an implantable medical device (IMD)operatively coupled to the sensor(s). The implantable medical deviceincludes: a neurologic signal module for receiving the neurologicsignal; an autonomic signal module for receiving the autonomic signal;and a processing element for determining whether an epileptic event hasoccurred based upon the first and second modal data.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention may be understood by reference to the followingdescription taken in conjunction with the accompanying drawings, inwhich like reference numerals identify like elements, and in which:

FIG. 1 provides a stylized diagram of a medical device implanted into apatient's body for providing a therapeutic signal to a structure of thepatient's body, in accordance with one illustrative embodiment of thepresent invention;

FIG. 2 provides a block diagram of a medical device system that includesa medical device and an external unit, in accordance with oneillustrative embodiment of the present invention;

FIG. 3A provides a block diagram of a cardiac signal module of a medicaldevice, in accordance with one illustrative embodiment of the presentinvention;

FIG. 3B provides a block diagram of a kinetic signal module of a medicaldevice, in accordance with one illustrative embodiment of the presentinvention;

FIG. 3C provides a block diagram of a detection module of a medicaldevice, in accordance with one illustrative embodiment of the presentinvention;

FIG. 4 shows the time of appearance (relative to clinical onset, dashedvertical line) and direction of deviations from reference activity of aplurality of body signals for four seizure types, specifically, absenceseizures, tonic-clonic seizures, and simple or complex partial seizures;

FIG. 5 shows time courses (relative to clinical onset, dashed verticalline) of activity of a plurality of body signals for tonic-clonicseizures;

FIG. 6 shows time courses (relative to clinical onset, dashed verticalline) of activity of a plurality of body signals for partial (simple orcomplex) seizures;

FIG. 7 shows time courses (relative to clinical onset, dashed verticalline) of activity of a plurality of body signals for idiopathic absenceseizures;

FIG. 8 shows (A) an exemplary two-dimensional plot of a trajectory ofepileptic movements, (B) an exemplary three-dimensional plot ofepileptic movements, and (C) an additional exemplary three-dimensionalplot of epileptic movements; and

FIG. 9 shows three two-dimensional, temporally cumulative plots ofdiscrete movements during the clonic phase of a primarily or secondarilygeneralized tonic-clonic seizure.

While the invention is susceptible to various modifications andalternative forms, specific embodiments thereof have been shown by wayof example in the drawings and are herein described in detail. It shouldbe understood, however, that the description herein of specificembodiments is not intended to limit the invention to the particularforms disclosed, but on the contrary, the intention is to cover allmodifications, equivalents, and alternatives falling within the spiritand scope of the invention as defined by the appended claims.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

Illustrative embodiments of the invention are described herein. In theinterest of clarity, not all features of an actual implementation aredescribed in this specification. In the development of any such actualembodiment, numerous implementation-specific decisions must be made toachieve the design-specific goals, which will vary from oneimplementation to another. It will be appreciated that such adevelopment effort, while possibly complex and time-consuming, wouldnevertheless be a routine undertaking for persons of ordinary skill inthe art having the benefit of this disclosure.

This document does not intend to distinguish between components thatdiffer in name but not function. In the following discussion and in theclaims, the terms “including” and “includes” are used in an open-endedfashion, and thus should be interpreted to mean “including, but notlimited to.” Also, the term “couple” or “couples” is intended to meaneither a direct or an indirect electrical connection. “Direct contact,”“direct attachment,” or providing a “direct coupling” indicates that asurface of a first element contacts the surface of a second element withno substantial attenuating medium there between. The presence of smallquantities of substances, such as bodily fluids, that do notsubstantially attenuate electrical connections does not vitiate directcontact. The word “or” is used in the inclusive sense (i.e., “and/or”)unless a specific use to the contrary is explicitly stated.

The term “electrode” or “electrodes” described herein may refer to oneor more stimulation electrodes (i.e., electrodes for delivering atherapeutic signal generated by an IMD to a tissue), sensing electrodes(i.e., electrodes for sensing a physiological indication of a state of apatient's body), and/or electrodes that are capable of delivering atherapeutic signal, as well as performing a sensing function.

Identification of changes in brain state (whether physiologic orpathologic) has traditionally been accomplished through analysis ofelectrical brain signals and behavioral observation. Continuous (e.g.,round-the-clock) automated monitoring of changes in brain state imposescertain limitations on the utilization of these traditional methods, dueto the difficulties inherent to automated ambulatory video, the largeamount of data produced per unit time, and the excessive demands onhuman and technical resources required to maintain an acceptablesignal/noise for electrical signals recorded from the scalp.Additionally, scalp signals have poor temporo-spatial resolution, acharacteristic which results in both low sensitivity and specificity ofstate-of-brain detection changes.

Implanted sensors or electrodes beneath the scalp but above the outerskull table or intra-cranial (epidural, subdural or depth) have beenused to overcome the limitations of scalp recordings. However, althoughthe quality of recordings (especially for intracranial electrodes) ismuch better (e.g., typically has a higher S/N) than that from scalpelectrodes, the quality is still limited and there are risks (e.g.,infection, bleeding, brain damage) associated with these devices, not tomention cost and scarcity of neurosurgeons to perform this type ofprocedures.

While electrical brain signals and behavioral observation may provideinformation for classification of brain states, this task can beaccomplished more efficiently, more precisely, and/or morecost-effectively through monitoring of other biological signals suchthose generated by the heart, muscle, skin, eyes, tympanic membranetemperature, and body posture/movement, since they may not requiresurgery, or if surgery is required for implantation, the procedures aremuch shorter, simpler, and cheaper that those required for recording ofbrain signals and there is no shortage of human resources.

Certain highly valuable neurological signals (e.g., cognitive) fordetection, quantification, and classification of state changes mayobtained non-invasively and can be used in this invention.

These multi-modal (e.g., autonomic, neurologic, etc) signals can be usedindividually or in combination to monitor continuously the brain andgenerate a state-of the-system/organ report, in real-time for thedetection, quantification, classification, validation, control andlogging of physiologic or pathologic state changes. This approach takesadvantage of the inherent and finely tuned dynamical coupling amongthese systems. For instance, changes in brain state/activity may resultin changes in heart activity, muscle activity, and skin properties.

Herein, Applicant describes a method, systems, and devices that may: a)detect in real-time pre-specified changes in brain state; b) quantifytheir duration, intensity, and time of occurrence; c) classify theirtype (e.g., epileptic vs. non-epileptic seizures; primarily vs.secondarily generalized seizures; generalized vs. partial seizures;complex vs., simple partial seizures; d) use as a basis for warning andcontrol/therapy, and/or e) save this information to memory for futureretrieval for optimization of detection, quantification andclassification of state changes and assessment and optimization oftherapeutic (e.g., control) efficacy. Non-epileptic movements in thisinvention refer to those resembling movements seen during tonic-clonicseizures but which are not caused by those seizures.

Herein, “multimodal” refers to epileptic event detection based on morethan one endogenous mode or type of signal. The multimodal epilepticevent detection disclosed herein provides a comprehensive,cost-effective, valuable alternative to systems of epileptic eventdetection exclusively based on brain electrical signals such as EEG. Todate, no multimodal systems have been developed or commercialized.Multimodal epileptic event detection may make use of signals or markersof autonomic, neurologic, endocrine, metabolic, gastro-intestinal,and/or dermal origin and of tissue/organ stress, such as those presentedin Table 1.

Multimodal detection of state changes takes advantage of the fact thatcertain brain structures directly or indirectly influence autonomic,endocrine, gastro-intestinal, dermal and metabolic functions and thatcertain abnormal states (e.g. seizures) stress the body tissues andresult in the elevation of certain compounds or molecules (e.g., stressmarkers) that may be used to detect and verify the occurrence of saidabnormal state.

It has been established that seizures in humans originating from orspreading to central autonomic structures induce changes in heart rate,among other cardio-vascular indices. It should be stated thatseizure-induced heart rate increases (which are far more frequent thanheart rate decreases) are not primarily the result of increased motoractivity or of metabolic changes, but are instead a neurogenicphenomenon. In the present invention, a highly robust, efficient andreliable system is provided for detecting, quantifying and/orclassifiying epileptic seizures based upon multi-modal signals and, ifdesired, using this information to provide warnings, therapies andoptimization of all of these tasks. Systems of the present invention aresuitable for commercial, long-term implants or external devices andprovide reliable and accurate indications of seizure events for a widevariety of epilepsy patients.

TABLE 1 Multimodal Signals Autonomic Cardiac: EKG, PKG,Echocardiography, Apexcardiography (ApKG), Intra-cardiac pressure,Cardiac blood flow, cardiac thermography; from which can be derived,e.g., heart rate (HR), change of HR, rate of change of HR, heart ratevariability (HRV), change of HRV, rate of change of HRV, HRV vs. HR.Also, blood pressure, heart sounds, heart rhythm, heartbeat wavemorphology, heartbeat complex morphology, and thoracic wall deflection.Vascular: Arterial Pressure, Arterial and venous blood wave pressuremorphology; Arterial and venous blood flow velocity, arterial and venousblood flow sounds, arterial and venous thermography Respiratory:Frequency, tidal volume, minute volume, respiratory wave morphology,respiratory sounds, end-tidal CO2, Intercostal EMG, Diaphragmatic EMG,chest wall and abdominal wall motion, from which can be derived, e.g.,,respiration rate (RR), change of RR, rate of change of RR. Also,arterial gas concentrations, including oxygen saturation, as well asblood pH can be considered respiratory signals. Dermal. Skin resistance,skin temperature, skin blood flow, sweat gland activity Concentrationsof catecholamines (and their metabolites) and acetylcholine oracetylcholinesterase activity in blood, saliva and other body fluidsconcentrations and its rate of change. Neurologic Cognitive/behavioral:Level of consciousness, attention, reaction time, memory, visuo-spatial, language, reasoning, judgment, mathematical calculations,auditory and/or visual discrimination Kinetic: Direction,speed/acceleration, trajectory (1D to 3D), pattern, and quality ofmovements, force of contraction, body posture, bodyorientation/position, body part orientation/position in reference toeach other and to imaginary axes, muscle tone, agonist-to- antagonistmuscle tone relation, from which can be derived, e.g., information aboutgait, posture, accessory movements, falls Vocalizations: Formed,unformed EEG/ECoG, Evoked potentials, field potentials, single unitactivity Endocrine: Prolactin, luteinizing hormone, follicle stimulationhormone, growth hormone, ACTH, cortisol, vasopressin, beta-endorphin,beta, lipotropin-, corticotropin-releasing factor (CRF) Stress Markers:Reactive oxygen and nitrogen species including but not limited to iso-and neuro-prostanes and nitrite/nitrate ratio, gluthatione, gluthationedisulfide and gluthatione peroxidase activity, citrulline, proteincarbonyls, thiobarbituric acid, the heat shock protein family,catecholamines, lactic acid, N-acetylaspartate, and metabolites of anyof the foregoing. Metabolic: arterial pH and gases, lactate/pyruvateratio, electrolytes, glucose

In one embodiment, the present invention relates to a method fordetecting an epileptic event based upon an autonomic signal (e.g., acardiac signal) and a neurologic signal (e.g., a kinetic signal) of apatient, comprising providing an autonomic signal indicative of thepatient's autonomic activity; providing a neurologic signal indicativeof the patient's neurological activity; detecting an epileptic eventbased upon the autonomic signal and the neurologic signal.

“Epileptic event” refers to a seizure, a period of increased likelihoodof a seizure, a pre-ictal period, or a post-ictal period, among others.

Any autonomic signal indicative of the patient's autonomic activity canbe used in the method. In one embodiment, the autonomic signal isselected from the group consisting of a cardiac signal, a respiratorysignal, a skin resistivity signal, an eye signal, a blood signal, andtwo or more thereof. The autonomic signal can be provided by anelectrocardiogram (EKG) device, a pupillometer, a face or bodytemperature monitor, a skin resistance monitor, a sound sensor, apressure sensor, a blood gas sensor, among others, or two or morethereof.

Any neurologic signal indicative of the patient's neurological activitycan be used in the method. In one embodiment, the neurologic signal isselected from the group consisting of a brain signal, a kinetic signal,and two or more thereof. The neurologic signal can be provided by anelectroencephalography (EEG) device, an electrocorticography (ECoG)device, an accelerometer, an inclinometer, an actigraph, aresponsiveness testing device or system, among others, or two or morethereof.

An epileptic event can be detected based upon the autonomic signal andthe neurologic signal. The partial basis upon the autonomic signal canmake use of techniques described in other patents or patentapplications, such as U.S. Pat. Nos. 5,928,272; 7,643,881; U.S. patentapplication Ser. No. 12/770,562, which are hereby incorporated herein byreference. The partial basis upon the neurologic signal can make use oftechniques described in other patents or patent applications, such asU.S. Pat. No. 7,630,757, which are hereby incorporated herein byreference.

In one embodiment, when the autonomic signal is a cardiac signal, thedetection can be partially based on the observation that some seizuretypes are associated with a change (e.g., increase) in heart ratecompared to a reference heart rate value range, such as a range ofmeasures of central tendency of heart rate over a short or relativelylong time window. Some other seizure types are associated with adecrease in heart rate above a reference heart rate value (see forexample, FIG. 4).

Generally, when the term “reference value” is used herein withoutfurther qualification, it refers to a value derived from an interictalperiod. Reference values or ranges thereof for any of the autonomic,neurologic, endocrine, metabolic or stress marker features are day oftime (e.g., circadian) and state (e.g., resting wakefulness) dependentand thus non-stationary. Although reference values for a certain featurein a certain state or time are most directly comparable to correspondingsignals in the same state or time, they may be comparable tocorresponding signals from other states, times, or both.

As used in FIGS. 4-7, clinical onset refers to the earlier of either a)when a patient notices a first seizure symptom, or b) when an expertobserver (or a person familiar with the patient's seizures) observes afirst change indicative of the seizure. It must be underscored thatwhile the most apparent change may be the “first” to be noticed by thepatient or seen by the observer, this change may have been preceded byother (unnoticed or unobserved) changes, and that the “first change”defining the seizure onset may not be the first change actuallyoccurring and associated with the seizure. Only one of several indiciaor signs of a seizure may be clinically recognizable, and the clinicalonset time is thus given or determined by this clinically recognizablesign or symptom. This does not preclude other clinical symptoms havingoccurred prior to the clinically recognizable sign or symptom. This isillustrated, for example, in FIG. 6, in which the onset of impairedresponsiveness precedes, by a few seconds, the clinical onset, and wherethe EKG and respiratory changes are also shown as occurring beforeclinical onset.

FIG. 4 shows the time of appearance (relative to clinical onset, dashedvertical line) and direction of deviations from interictal referenceactivity, of a plurality of body signals for four seizure types:specifically, absence seizures, generalized tonic-clonic seizures(whether primarily or secondarily generalized), and simple or complexpartial seizures. The horizontal arrows show the times of appearance ofthe symptom change in reference to clinical onset as defined in thepresent application. A dot without horizontal arrows indicates that themost important aspect of the signal change occurs at clinical onset.This does not exclude the possibility that this change may reappear orchange direction at some later time. Upward vertical arrows indicate anincrease in the value of the signal while downwards arrows indicate adecrease in value. Arrow length does not reflect a scale or magnitude ofthe change. When multiple deviations are shown, the larger, thickerarrow is the one most commonly seen over general patient populations. Ofcourse, the skilled epileptologist is aware that some patients will showone or more variations from the typical cases shown in FIG. 4.

To facilitate understanding, certain important details about certainbody signals, their onset, and temporal evolution in reference toclinical onset (dashed lines) have been omitted from FIGS. 4-7. Thesefigures should be viewed only as illustrative of the changes in bodysignals that occur with the various seizure classes.

For example, tonic-clonic seizures are often correlated with an increasein heart rate beginning at about seizure onset (see for example, FIG.5).

For another example, partial seizures are often correlated with anincrease in heart rate beginning before, at, or shortly afterelectrographic seizure onset. The increase is less than that associatedwith tonic-clonic seizures (see for example, FIG. 6).

In another embodiment, when the autonomic signal is a respiratorysignal, the detection can be partially based on the observation thatsome seizure types are associated with a deviation of the respirationrate from a reference respiration rate value range (see for example,FIG. 4).

For example, partial seizures are often correlated with increases inrespiration rate (see for example, FIG. 6).

In one embodiment, when the autonomic signal is a skin resistivitysignal, the detection can be partially based on the observation thatsome seizure types are associated with a deviation of skin or bodytemperature from an interictal reference skin or body temperature valuerange (see for example, FIG. 4).

For example, certain partial seizures are associated with a decrease inskin resistivity (see for example, FIG. 6) and tonic-clonic seizureswith an increase in body temperature.

In still another embodiment, when the neurologic signal is an eyesignal, the detection can be partially based on the observation thatsome seizure types are associated with eye position changes (e.g.,forced binocular deviation to the right) or the occurrence of abnormaleye movements (e.g., horizontal nystagmus) or both) (see for example,FIG. 4). For example, absence seizures are associated with quasiperiodicblinking (see for example, FIG. 7).

The rate, amplitude and pattern of eyelid blinking may provideinformation about level of consciousness (e.g., awake vs. asleep orunresponsive) of a patient and during wakefulness. These parameters mayallow for differentiation of normal vs. abnormal wakefuleness states,e.g., abnormal wakeful state during complex partial and/or absenceseizures, the state following termination of complex partial and/orabsence seizures, and/or the termination of generalized tonic clonicseizures. Parameters such as blinking rate, amplitude and inter-blinkinginterval (from which distinctive patterns may be discerned) may be usedfor detection and quantification of seizures as well as forclassification purposes through comparisons with the non-seziureinterictal state. Blinking activity, which is a form of kineticactivity, may be recorded using device(s) (e.g., electrodes) placed overor under the skin overlaying the supra- or infraorbital regions or withoptical devices.

In one embodiment, when the autonomic signal is a blood signal, thedetection can be partially based on the observation that some seizuretypes are associated with an increase in stress markers (e.g.catecholamines, cortisol, and metabolites thereof) relative to areference level of the stress marker.

Should stress markers reach a prespecified reference value (which may bedifferent than that used for detection of state change purposes), thepatient's total antioxidant capacity and lipid peroxidation intensitymay be monitored to institute neuroprotective measures, such asincreasing total antioxidant capacity. Neuronal hyper-excitability whichoccurs in seizures may lead to excessive production of free radicals andeventually to neuronal injury.

Alternatively or in addition, the blood signal can be a blood gas (e.g.,O2 or CO2) level or a blood pH level, and the detection can be partiallybased on the observation that some seizure types are associated withblood gas and/or pH levels outside of an interictal reference valuerange (see for example, FIG. 4). One or more of the blood signalsdescribed above may give information regarding respiratory signals, andvice versa.

For example, tonic-clonic seizures are associated with a drop inarterial O2 concentration, an increase in arterial CO2 concentration,and a decrease in blood pH (see for example, FIG. 5).

For another example, certain partial seizures are associated with aslight increase in arterial O2 concentration, a decrease in arterial CO2concentration, and a slight increase in arterial pH (see for example,FIG. 6).

In one embodiment, when the neurologic signal is a brain signal, thedetection can be partially based on the observation that some seizuretypes are associated with sudden, transient increases in the amplitudeat certain frequencies of cortical waves or with changes in theirmorphology (e.g., spike-slow wave complexes) (see for example, FIG. 4).

In one embodiment, when the neurologic signal is a kinetic signal, thedetection can be partially based on the observation that some seizuretypes are associated with increases or decreased in the amplitude andvelocity of movements the appearance of particular patterns or sequencesof body or appendicular movements, cessation of movements or loss ofpostural tone or marked increased in body muscle tone as provided byelectromyography (EMG), accelerometer, inclinometer, and/or actigraphoutputs (see for example, FIG. 4). EMG anti-gravitatory muscles providessimilar information to accelerometers or inclinometers about falls andin certain cases, EMG may replace them. For example, if a patient is inthe recumbent position and has a generalized tonic-clonic seizure, theinclinometer and the accelerometer will not detect a fall but the EMGwill (indirectly) by showing absence of muscle activity inantigravitatory muscles. This also applies to patients that at the onsetof the generalized tonic-clonic seizure are either propped/supported.Falls during certain generalized tonic clonic seizures are caused byincreases not decreases in postural muscle tone. Also, while muscle tomemay be decreased or increased during partial seizures, the extent(number and type of muscle groups involved compared to generalizedtonic-clonic seizures) allows for differentiation. Particular examplesof kinetic signals relating to epileptic movements are shown in FIGS.8-9.

U.S. Patent Application Publication 2009/0124870 to Arends et al.discloses a patient monitoring system using at least one heart ratesensor and a least one muscular tension sensor. The publication does notdisclose acquisition or analysis of kinetic activity to monitor apatient. One or more of the embodiments of the present invention providefor detecting seizures through cardiac data (e.g., EKG) used inconjunction with motion data (e.g., accelerometer).

FIG. 8A shows a two-dimensional (x,y) discrete trajectory of epilepticmovements (low sampling rate is used to minimize computations but acontinuous trajectory may be plotted). This plot contains spatial (inreference to a fiducial marker such as the patient's sternum) andtemporal information (when a movement occurs and their order ofoccurrence) about body movements during an epileptic seizure. The arrowsshow the sequence of movements. Colors or shapes, instead of arrows maybe used to track the temporal evolution of movements. When stereotypicalthe movement trajectory may be used as a template for detection usingfor example matched filtering. This plot may be also generated in 3-D.

FIG. 8B shows a three-dimensional (x,y,z) discrete plot of epilepticmovements The movements form clusters (3 in this example; the left mostand lower most clusters are intended to illustrate interictal movementsand the right most cluster, epileptic movements) that may have differentshapes or dimensions for each patient. These clusters may be used (e.g.,cluster analysis, principal component analysis) for detection,quantification, classification and/or validation of detection ofseizures, and optionally as well as for logging, tracking the temporalevolution of seizures, and/or optimization of detection, quantification,classification, and/or of therapy. This plot contains only spatialinformation; temporal information may be added through the use of arrowor color or shape codes. When stereotypical the movement trajectory maybe used as a template for detection using for example matched filtering.

FIG. 8C shows a three-dimensional (x,y,z) discrete plot of epilepticmovements; notice that one movement occurs only in 2-D (low samplingrate is used to minimize computations but a continuous trajectory may berecorded. This plot contains only spatial information (in reference to afiducial marker such as the patient's sternum); temporal information maybe added through the use of arrow or color or shape codes. Whenstereotypical the movement trajectory may be used as a template fordetection using for example matched filtering.

FIG. 9 shows three two-dimensional, temporally cumulative plots ofdiscrete movements during the clonic phase of a generalized (primarilyor secondarily) tonic-clonic seizure. The first movement in the sequenceis located closest to the x,y axes intersection and subsequent ones areplotted to the right of the preceding movement and in the order in whichthey occur. For ease of visualization there are 3 plots ((A) x,y; (B)y,z; (C) x,z). The vertical and horizontal axes provide informationabout amplitude and the horizontal axis also provides temporalinformation (e.g. inter-movement interval). In this illustration, themovements occur at equal time intervals and are periodic as is common inthe clonic phase of a generalized seizure. When stereotypical themovement trajectory may be used as a template for detection using forexample matched filtering.

U.S. Patent Application Publication 2009/0137921 to Kramer et al(Kramer) describes using accelerometer data to compare againstpreviously stored motion data that are not confined to epileptic events.One or more of the embodiments of the present invention provide fordetecting seizures through cardiac data (e.g., EKG) used in conjunctionwith motion data (e.g., accelerometer). Embodiments of the presentinvention may provide for detecting seizures using less specific motiondata since the cardiac and motion data may be used to confirm eachother.

Herein, one or more of the direction, speed/acceleration, trajectory (1Dto 3D), pattern, and quality of movement may be termed a characteristicof movement. Such characteristics of movement may be determined forparticular movements and used to distinguish among ictal, post-ictal,and interictal motor activity.

For example, absence seizures are typically correlated with a cessationof body movements and temporary but complete loss of responsiveness andawareness (see for example, FIG. 7).

For another example, tonic-clonic seizures are associated with losses ofresponsiveness and awareness, and falls to the ground if the patient isstanding at onset. Common characteristics of movement include a “spike”in the inclinometer's output at seizure onset (e.g., if the patient wasstanding, the seizure will cause him to fall), a quiet period ofaccelerometer output after seizure onset (e.g., the tonic phase), and aseries of quasiperiodic “spikes” (e.g., at around 3 Hz) in accelerometeroutput after the tonic phase (e.g., the clonic phase), followed bycessation of body movements. The tonic phase presents with a markedincrease in EMG activity in axial and appendicular muscles. Also, a“spike” in inclinometer output during or after the post-ictal phase maybe seen (e.g., the patient rises after a fall at seizure onset) (see forexample, FIG. 5).

For yet another example, certain partial seizures are often correlatedwith a quiet period of accelerometer output after seizure onset (see forexample, FIG. 6), while others characterized by an increase ininvoluntary movements and vocalizations (e.g., so called “hypermotoric”seizures).

Generally, movement characteristics, qualities, and loci are similar, ifnot stereotypical, among tonic-clonic seizures and certain partialseizures for a particular patient, and are also similar among patientswith these seizure types. Thus, patterns can often be obtained and usedfor detection, quantification, and classification. However, in certainpartial seizures, movements may differ not only between patients butalso between seizures of the same patient.

In some embodiments of this invention, the number, type, and placementof motion sensors to be used in detecting, quantifying, and/orclassifying movement can be based on (a) degree of movement similaritybetween seizures, (b) the signal-to-noise ratio of data from the locusor loci (e.g., body parts such as eyes, head, limbs, trunk, etc.),and/or (c) patient safety and device longevity considerations, amongothers. These considerations can be taken into account to maximize speedand/or accuracy of detection, quantification, and/or classifying, and/orperforming this task or tasks in a monetary and/or computationallycost-effective manner.

For example, if a patient's tonic-clonic seizures are consistentlypreceded by a deviation of the head to the right, a single motion sensor(e.g., placed in this case on the head or over/in a neck muscle involvedin the movement) may be sufficient to detect the motion and characterizethe seizure. If the patient's seizures are characterized by suddenfalls, again, a single device, placed in a body part that will have mostacceleration or range of displacement, may be sufficient for seizuredetection, quantification and classification purposes. If the patient'sseizures are frequently secondarily generalized seizures, a plurality ofdevices, with at least one situated on each of the left and right sidesof the body and/or with at least one situated on the upper and lowerportions of the body may be desirable to provide sufficient sensitivityand specificity for seizure detection and characterization.

The choice of number of sensors, their type (e.g., whether they aresensitive to mechanical or electrical signal changes), and theirplacement can be optimized for each seizure type and patient.

In one embodiment, when the neurologic signal is a brain signal, thedetection can be partially based on the observation that some seizuretypes are typically correlated with a decrease in responsiveness (seefor example, FIG. 4).

For yet another example, partial seizures can often be distinguishedbetween simple and complex based on changes in the patient'sresponsiveness. Simple partial seizures are associated with preservationof awareness and memory for the events that occurred during the seizureand responsiveness may or may not be preserved, whereas complex partialseizures are invariably characterized by impairment in the patient'sunawareness of their surroundings and anterograde amnesia spanning acertain time period (see for example, FIG. 6). Responsiveness is testedby having the patient perform certain motor actions (e.g., press abutton; raise an arm) and/or cognitive tasks (e.g., answer questions).Awareness may be tested by measuring a patients ability to recollectevents that occurred during a certain period of time or by administeringmemory tests. The number of words, images or events correctly recalledallow quantification of the degree of awareness (compared to aninter-ictal reference value). In one embodiment, the method furthercomprises providing a responsiveness test and awareness tests to assesspatient's responsiveness and awareness, and characterizing the epilepticevent based upon the speed and appropriateness or correctness of theresponses to neuropsychologic tests.

For example, the following table shows conclusions that can generally bedrawn from determinations of whether a patient remains responsive(“Responsive?”) and/or remains aware (“Aware?”) during a seizure.

Responsive? Aware? Reasonable conclusion Yes Yes Not a complex partialor secondarily generalized seizure Yes or No No Complex partial seizureNo Yes Simple partial seizure interfering with ability to respond No NoComplex partial or primarily or secondarily generalized seizure

The autonomic signal and the neurologic signal can be used to detect aseizure; to quantify its severity; to classify a seizure as to its type(e.g., absence, tonic-clonic, simple partial, complex partial); and/orto validate an identification or detection of a change of state ascorresponding to a seizure.

The features of the two or more signals on which a detection or otheraction of the present invention is based may occur simultaneously or inany temporal relationship. In one embodiment, the temporal relationshipbetween two signals is as set forth in FIGS. 4-7 and as described above.Relative temporal relationships between the body signals may be usedindentify, validate, classify, and/or quantify an epileptic event.Information relating to the timing of any two body signals, e.g., anincrease in heart rate before, after, or substantially simultaneouslywith accelerometer data suggestive of a seizure, may be used to identifyan epileptic event, validate an identification of an epileptic event,quantify an epileptic event's severity, intensity, or duration, and/orclassify a seizure.

The various embodiments recited above may be also used to distinguishepileptic generalized from non-epileptic generalized seizures whosekinetic activity, but not patho-physiology, resembles that of epilepticseizures. Non-epileptic generalized seizures, also known aspseudo-seizures, psychogenic seizures, or hysterical seizures, are oftenmisdiagnosed as epileptic at large cost to the patient, caregivers, andthe health care system. A multimodal signal approach relying heavily onkinetic, autonomic and metabolic signals is ideally suited fordiagnosing identifying and classifying seizures as non-epileptic givenits high sensitivity and specificity and cost-effective (no hospitaladmission would be required as this invention's methods areimplementable in small portable devices).

The following are a few examples of differences with high discriminatoryvalues, one or more of which can be used to distinguish betweenepileptic generalized seizures and non-epileptic generalized seizures:a) The intensity of non-epileptic movements, unlike that of epilepticmovements, waxes and wanes (crescendo-decrescendo pattern) throughoutthe event; b) Non-epileptic movements, unlike epileptic movements, aremulti-directional or multi-planar, said changes in direction occurringvery rapidly and in a random sequence. For example, vertical movementsmay give way to horizontal ones and these in turn to oblique or rotaryor flapping movements; c) Joint movements in non-epileptic seizures,unlike in epileptic seizures, are incoherent or disorganized: while theright upper extremity is moving in the vertical plane at a certain speedand with certain amplitude and phase, the direction, speed, phase andamplitude of movement of the left upper extremity may be different atthe same time; d) in non-epileptic seizures, unlike in epilepticseizures, co-activation of agonists and antagonists muscle groups israrely seen: Co-activation of the abdominal and paraspinal musclesduring an epileptic generalized tonic-clonic seizure keeps the torsostraight while the sole activation of paraspinal muscles, a commonobservable in non-epileptic generalized seizures, manifests as an archedback; e) Involvement (in the form of movements) of certain body parts iscommonly found in non-epileptic seizures while they are rarely if everseen in epileptic generalized seizures; pelvic thrust, pelvic gyrations,and other pelvic movements are nearly pathognomic of non-epilepticseizures; f) Metabolic (lactic) acidosis occurs with epilepticgeneralized tonic-clonic seizures and not with non-epileptic generalizedseizures.

Detection can be conducted by any appropriate technique. For example,each signal may be recorded, conditioned, and processed using hardware(e.g., DC or AC amplifiers), gains or amplification, filters andsampling rates appropriate for the spectral properties, and/ortime-scale and characteristics of each signal. Each signal may beanalyzed whole or after decomposition using suitable digital or analogsignal processing techniques. The decomposition may be performed usingany of the following techniques: Fourier transform based methods,wavelets, customized FIR or IIR filters, intrinsic time scaledecomposition, wavelet transform maximum modulus, or any other techniquewhich may decompose the signal based on its spectral properties,morphology or waveform, site of origin or generation, its positionregarding a baseline, zero-crossings and circadian or ultradian rhythms.In the case of a decomposed signal none, one, or more of the componentsmay be discarded if it is deemed of little value for detection of changeof brain/body state. These data, as they stream through the system, maybe analyzed in windows of appropriate length for each signal (e.g.,signal-based customized window approach). This window corresponds to aforeground which may be referenced for quantitative purposes to abackground, consisting of past data. The length of the background windowmay be determined by the properties of the signal under study and thetime scale of the patterns or events which are the subject of detection.Any of these features or parameters may be adapted as needed to accountfor circadian or other influences to the signals

Although the above paragraph emphasizes hardware for signal conditioningand other tasks, the person of ordinary skill in the art is aware thatsoftware, firmware, or other implementations of one or more of thetechniques discussed above may be used.

Change in a given signal feature may be associated with one, more thanone or none of the brain/body state changes of interest. Reciprocally,changes in brain state may be associated with only one signal featurechange, two or more, or none. Each signal feature may be trackedindividually and its changes may be subjected to statistical, cluster,Poincaré plot, and/or other forms of analyses to identify changes whichare significantly correlated with pathological brain/body state changessuch as seizures, movement disorders or with physiological ones such assleep, attention or cognitive processes. These analyses may yieldselectivity (Sl), sensitivity (Se) and specificity (Sp) for each signalfeature and may be used to assign values to each of them. Thetype/number of signal feature used for on-line, real-time detection ofchanges in brain/body state may be chosen using a value system based onthe degree of selectivity (Sl), sensitivity (Se) and specificity (Sp) ofeach signal feature.

Selectivity is given by the fraction or proportion of type of statechanges with which a signal feature is associated. For example, if astate change is associated with only one signal feature change theselectivity would be 1 (1/1) or if it is associated with 2, selectivitywould be 0.5 (½).

Sensitivity is given by the fraction of detected changes in signalfeature over all detectable changes in signal features; for example, ifthere are 20 state changes/day and 10 of these are detected using acertain signal feature, sensitivity for this signal feature in this casewould be 0.5 (10/20).

Specificity is given by the fraction of detected true changes over alldetections; for example, if there were 20 detections using a certainsignal feature but only 10 corresponded to true brain/body statechanges, the signal feature's specificity would be 0.5 (10/20).

The value (for detection purposes) of each signal feature for aparticular brain or body state (e.g., ictal, pre-ictal, post-ictal, orinterictal, among others) (f) is the product of selectivity (Sl),sensitivity (Se) and specificity (Sp); [f=Sl×Se×Sp]. The closer thevalue to 1 (which is the maximum possible), the more powerful the signalfeature may be considered to be. This value, f, may determine the numberof signal features necessary to maximize the probability of accurate andreliable detection of brain/body change of state: Any signal featurewith a value of 1 may be used as the only detection signal feature.Signal features with value f<1 may be sorted out, ranked, and added toobtain a summed weighted index F, (F=fEKG+f EOG+ . . . +f TT). F may becalculated in real-time, if the computations required can be performedat an acceptable power expense, or off-line if this is not the case. Forexample, if data analysis in a certain subject reveals that seizures areaccompanied by acceleration in heart rate and this change hasselectivity, sensitivity and specificity of 1 so that f EKG=1, thissignal feature may be chosen as the only one for automated detectionpurposes. However, if fEKG<1, other f values, as many as needed ordesired, may be added, to reach or approach a desirable high value, suchas 1, if the computations can be performed in real-time at an acceptablepower cost.

For a particular example, consider a situation where the signal featurevalues are [fEOG=0.40; fEKG=0.45; fPKG=0.10; fSkT=0.15; fEMG=0.12;fAt=0.14; fTT=0.09]; these values are sorted out and ranked from highestto lowest [fEKG=0.45; fEOG=0.40; fSkT=0.15; fAt=0.14; fEMG=0.12;fPKG=0.10; fTT=0.09] and as many values as needed are added to obtainF=˜1 (e.g., in this case, F=0.45+0.40+0.15=1; in this case 3 valuessufficed to reach 1. It should be noted that in certain cases, additionof all indices may not equal 1, in which case detection of state changesmay be issued if and when F is greater than a lower number, such as 0.5.Threshold (T), duration (t) other constraints (such as time of day orpatient's state (awake vs. asleep; resting wakefulness vs. exercise) maybe introduced to improve accuracy of detection, if desired.

Another metric of interest is the property defined herein as degree ofsignal ubiquitousness (U) denoting the number of different states duringwhich the same signal feature changes. For example, cardiac activity ishighly ubiquitous since increase in heart rare occur during differentnormal (exercise, emotion) and abnormal states (seizures, cardiacarrythmias), while metabolic (lactic) acidosis would be absent from all,save only generalized tonic-clinic seizures and in extreme/enduranceexercise, a rare practice. Ubiquitousness (U) is the fraction of asignal feature present in various states over the total number ofstates. The ubiquitousness (U) of heart rate in the example above, is 1since it occurs in 4/4 states while that of lactic acidosis is 0/4 or atmost ¼ if extreme exercise is included. U may replace Sl in thecalculation of F to optimize performance of state detection changes.While in certain cases U is the reciprocal of Sl. There may be certaincases in this relation does not apply and U maybe more useful. Asstated, in one embodiment, detecting the epileptic event is based on anautonomic signal and a neurologic signal. In one embodiment, analgorithm can receive as inputs both the autonomic signal and theneurologic signal and process them together to yield an outputindicative of a detection or a non-detection. In another embodiment,detecting the epileptic event comprises identifying a putative epilepticevent based on one of the autonomic signals (e.g., cardiac signals asmeasured by EKG) and one of the neurologic signals (e.g., kineticsignals as measured with an accelerometer, an inclinometer, or anactigraph), and validating the identifying based on one of the other ofthe autonomic signals (e.g., respiration) and one of the otherneurologic signal (e.g., responsiveness test, memory test, comprehensiontests, etc.).

Another metric of interest is Positive Predictive Value (PPV), definedas:

(number of True Positives)/(number of True Positives+number of FalsePositives)

Regardless how the detection is made, in one particular embodiment, thepresent invention relates to a method for detecting an epileptic eventbased upon a patient's cardiac signal and kinetic activity, comprisingproviding a cardiac signal indicative of the patient's heart beats;providing a kinetic signal indicative of a body movement of the patient;and detecting an epileptic event based upon the cardiac signal and thekinetic signal.

The cardiac signal may be electrical, acoustic, thermal, or any othercardiac signal detectable using certain equipment or tools. In oneembodiment, the cardiac signal is provided by an electrocardiogram(EKG).

The kinetic signal can be provided by a device capable of recording anyof the attributes inherent to movement such as amplitude, velocity,direction, trajectory and quality. In one embodiment, the kinetic signalis provided by an accelerometer, an inclinometer, or an actigraphicdevice. An actigraphic device or actigraph can be considered as beingboth an accelerometer and an inclinometer. An exemplary plot oftrajectories is shown in FIG. 8A. An exemplary plot of clusters ofpositions is shown in FIG. 8B.

In one embodiment, the present invention relates to a method fordetecting an epileptic event based upon a patient's cardiac signal andkinetic activity, comprising providing a kinetic signal indicative of abody movement of the patient; calculating based on the kinetic signal akinetic score indicative of a correlation of said kinetic signal with anepileptic event; detecting an epileptic event based upon the patient'sheart beat sequence; and providing an output indicative of an epilepticevent based on the kinetic score.

In another embodiment, the method comprises providing a cardiac signalindicative of a cardiac activity of the patient; calculating based onthe cardiac signal a cardiac score indicative of a correlation of saidcardiac signal with an epileptic event; detecting an epileptic eventbased upon the patient's kinetic activity; and providing an outputindicative of an epileptic event based on the cardiac score.

These are both examples of detecting an epileptic event based uponmultimodal data, using a plurality of modes of data (e.g., a cardiacmode signal and a kinetic mode signal).

The kinetic signal indicative of a body movement of the patient can beas described above. A kinetic score can be calculated and/or the kineticsignal can be classified as either an epileptic event kinetic signal ora non-epileptic event kinetic signal based on the practitioner'sknowledge (e.g., the practitioner is aware certain kinetic signals,e.g., inclinometer spikes, periods of increased accelerometer activity,periods of decreased accelerometer activity, periods of actigraphactivity outside of normal ranges, timewise correlations of suchsignals, etc.), by prior correlation of a patient's kinetic signals withhis or her seizures identified by autonomic (e.g., EKG), neurologic(e.g., EEG or direct or indirect clinical observation) endocrine,metabolic (e.g., pH), stress marker (e.g., cortisol) etc., or acombination thereof.

Imaging (e.g., video, thermography, etc.) and/or audio recordings of thepatient may be used qualitatively or quantitatively to detect and/orvalidate the detection of seizures. Detection or validation may be madeon- or off-line via human visual analysis or using algorithms thatcompare one or more of position, velocity, direction, or trajectory ofmovement of any body part during seizures to non-seizure movements. Thetime between consecutive movements, the total duration of epilepticmovements, and/or their quality (e.g., jerky or smooth) may be also usedfor detection and/or validation of seizures.

Detecting a possibility of an epileptic event based upon the patient'sheart beat sequence can make use of the cardiac-based seizure detectionapproaches discussed above. For example, noting an increase in thepatient's heart rate relative to an interictal reference value is oneembodiment of “detecting an epileptic event,” e.g., a period ofincreased likelihood of a seizure.

Upon classifying the kinetic signal and detecting the possibility of theepileptic event based upon the patient's heart beat sequence, an outputindicative of an epileptic event can be provided if the kinetic signalis classified as an epileptic event kinetic signal; and an outputindicative of the non-occurrence of an epileptic event can be providedif the kinetic signal is classified as a nonepileptic event kineticsignal. This method can validate a cardiac-based seizure detection byuse of kinetic signals.

In another embodiment, the present invention relates to a method fordetecting an epileptic event based upon a patient's cardiac signal andkinetic activity, comprising providing a kinetic signal indicative of abody movement of the patient; classifying the kinetic signal as eitheran epileptic event kinetic signal or a nonepileptic event kineticsignal; detecting an epileptic event based upon changes in the patient'sheart beat sequence; confirming the detecting if said kinetic signal isclassified as an epileptic event kinetic signal; overriding thedetecting if said kinetic signal is classified as a nonepileptic eventkinetic signal; and providing an output indicative of an epileptic eventonly if the detecting is confirmed.

In yet another embodiment, the present invention relates to a method fordetecting a tonic-clonic epileptic seizure whether primarily orsecondarily generalized (i.e., whether the seizure emerges in bothhemispheres of the brain at substantially the same time (primary) orwhether it emerges at a particular focus and then spreads (secondary)based upon two or more of a patient's body signals, comprising:providing at least two body signals selected from the group consistingof a cardiac signal indicative of the patient's heart beats; anaccelerometer signal indicative of the patient's movement; aninclinometer signal indicative of the patient's body position; anactigraph signal indicative of the patient's movement, body position, orboth; a respiratory signal indicative of the patient's respiration; askin resistivity signal indicative of the patient's skin resistivity; anblood gas signal indicative of the patient's blood oxygen content,carbon dioxide content, or both; a blood pH signal indicative of thepatient's blood pH; an isometric force signal indicative of thepatient's muscle activity; a sound signal indicative of the patient'soral utterances or vocalizations; an ocular signal indicative of thepatient's eye position and movements; a responsiveness signal indicativeof the patient's responsiveness; and a stress marker signal indicativeof at least one stress marker of the patient; and detecting thegeneralized tonic-clonic epileptic seizure based upon the timewisecorrelation of two features, one feature being of each of the at leasttwo body signals, wherein:

the feature of the cardiac signal is an increase in the patient's heartrate above a reference value;

the feature of the accelerometer signal is an increase in the velocity,amplitude or number of movements per unit time of said patient above areference value followed by a decrease in the patient's movement below areference value;

the feature of the inclinometer signal is a rapid change of thepatient's body position indicative of a fall;

the feature of the respiratory signal is a brief apnea followed byhypopnea and after the end of the seizure by a transient increase inrespiratory rate, tidal volume, and minute volume;

the feature of the skin resistivity signal is a skin resistivity outsidean interictal reference value range;

the feature of the blood gas signal is a decrease in the patient'sarterial blood oxygen content below an interictal reference value and anincrease in arterial carbon dioxide content above an interictalreference value, or both;

the feature of the blood pH signal is a decrease in the patient's bloodpH below an interictal reference value;

the feature of the isometric force signal or EMG activity is a transientincrease in the patient's muscle contractions above an interictalreference value that may be preceded or followed depending on theseizure type (e.g., primarily vs. secondarily generalized) and patientby a loss of muscle tone or of EMG in anti-gravitatory muscles

the feature of the sound signal is a distinctive sound referred to as apatient's an “epileptic cry”;

the feature of the ocular signal is a deviation of the patient's eye'soften upwardly;

the feature of the responsiveness signal is a decrease in the patient'sresponsiveness below an interictal reference value;

and the feature of the stress marker signal is an increase in at leastone stress marker of the patient above an interictal reference value.

In one embodiment, this method further comprises indicating the end ofthe generalized tonic-clonic epileptic seizure when the value of one ormore of the features listed above falls outside the range of values forboth the ictal and inter-ictal states for that patient.

Alternatively or in addition, in one embodiment, this method furthercomprises indicating the beginning of a post-ictal period based upon theappearance of at least one post-ictal feature of at least one said bodysignal, wherein:

the post-ictal feature of the cardiac signal (e.g. heart rate) isoutside the range of values for the ictal and interictal periods forthat patient and within the range of postictal values;

the post-ictal feature of the accelerometer signal is a cessation of thepatient's seizure movements; the post-ictal feature of the respiratorysignal is a transient increase in the patient's respiratory frequency,tidal volume, and/or minute volume, such as may follow periods of apneaand hypopnea and the patient's seizure movements; and

the post-ictal feature of the responsiveness and of the awareness signalis the persistence in the patient's unresponsiveness and unawareness. Asshould be apparent, “unresponsiveness” and “unawareness” are distinctphenomena; a person may be aware of stimuli (and able to form memoriesof said stimuli and the context in which they occurred) but unable torespond thereto, and vice versa. Responsiveness as used herein refers tothe ability to respond reflexively or adaptive/purposefully; thisdistinction may be used for seizure classification purposes.

The term “post-ictal,” is not necessarily limited to the period of timeimmediately after the end of the primarily or secondarily generalizedtonic-clonic epileptic seizure and is not limited to this type ofseizure but also encompasses partial seizures (e.g., all complex andcertain simple partial and absence seizures). Rather, it refers to theperiod of time during which at least one signal has one or more featuresassociated with the period following the cessation of a seizures thatindicates one or more of the patient's body systems are not functioningnormally (e.g., as a result of the seizure or of an injury sufferedduring the seizure) but are not exhibiting features indicative of aseizure.

In a further embodiment, this method further comprises indicating theend of the post-ictal period and the beginning of the inter-ictal periodwhen the values of at least one of the post-ictal features changes tobeing within the range of reference body signal values or behaviorassociated only with the inter-ictal period wherein:

the cardiac signal returns to a heart rate within a range indicative ofan interictal state for said patient

the accelerometer signal of the patient's movement velocity, amplitudeor number of movements per unit time returns to values indicative of aninter-ictal state for said patient;

the accelerometer signal is a movement pattern including inter-movementintervals or trajectory indicative of that patient's inter-ictal period

the respiratory signal (e.g., rate, tidal volume, minute volume, andpattern) returns to a range indicative of the inter-ictal state for thatpatient; the responsiveness signal returns to its range of inter-ictalvalues for that patient; and

the patient's awareness returns to inter-ictal ranges for the patient.

The changes in signal features (e.g., responsiveness, awareness, heartactivity, respiratory activity, etc.) during the transitions (e.g.,inter-ictal to ictal, ictal to post-ictal and post-ictal to interictal)that make up the epileptic cycle, may or may not occur simultaneously orsynchronously; certain signal feature values change ahead or behindothers. Thus, using these signal features, the transitions may bequalitatively classified into (a) partial or complete; (b)quantitatively as the fraction of signal features (numerator) thattransitioned into or out of the state over the total number of signalfeatures that have been observed (denominator).

For example, if only 2/4 signal feature values indicative of thetransition from ictal to postictal or from post-ictal to inter-ictalhave reached values within the range of the new state, the is classifiedas partial assigned an score of 0.5 and declared complete only when all(e.g., 4/4 in this example) signal features values are within the rangeof indicative of the new state at which time the transition is deemedcompleted.

The transitions may be also quantified using the: a) magnitude of thechange in feature signal values measured for example as the increase inseizure energy (see Osorio et al, Epilepsia 1998, 2001) as compared toits inter-ictal value, or the percent of incorrect responses to acomplex reaction time test compared to the responses in the inter-ictalstate, or the lengthening in response time regardless of correctness ofresponses (see, e.g., U.S. Ser. No. 12/756,065, filed Apr. 7, 2010,which is hereby incorporated herein by reference) compared to thatrecorded in the inter-ictal state for that patient; b) rate of change inthe signal features measured for example as the time to peak valuechange measured from the onset time of the transition or the time tofirst error in a complex reaction time compared to those obtained in theinter-ictal period; c) duration (e.g., in seconds) of the state changefrom the onset of the inter-state transition to the beginning of thetransition from the present state (e.g., ictal) to another state (e.g.post-ictal). These metrics may be used to e.g., assess the disease state(e.g., the duration and magnitude of the ictal state are increasing overtime) and also the efficacy of therapeutic interventions. Shortening themagnitude of the changes (e.g., degree of unresponsiveness) in signalfeature values from the inter-ictal range to the ictal value or thetransition time between the post-ictal and interictal periods provideevidence that the therapy is beneficial while increases in the magnitudeor duration of the changes in feature signals from the inter-ictal rangeto ictal value or a lengthening of the transition from the post-ictal tothe interictal state are evidence of an adverse therapeutic effect. Thequalitative and quantitative categorization of the various states and oftheir transitions is applicable to all seizures and epilepsy types andalso to other states and inter-state transitions.

Analogously to detecting tonic-clonic seizures, in one embodiment, thepresent invention relates to a method for detecting a partial epilepticseizure based upon two or more of a patient's body signals, comprisingproviding at least two body signals selected from the group consistingof a cardiac signal indicative of the patient's heart beats; anaccelerometer signal indicative of the patient's movement; aninclinometer signal indicative of the patient's body position; anactigraph signal indicative of the patient's movement, body position, orboth; a respiratory signal indicative of the patient's respiration; askin resistivity signal indicative of the patient's skin resistivity; anarterial blood gas signal indicative of the patient's blood oxygencontent, carbon dioxide content, or both; an arterial blood pH signalindicative of the patient's blood pH; a sound signal indicative of thepatient's oral utterances or vocalizations or breathing; aresponsiveness signal indicative of the patient's responsiveness; anendocrine signal indicative of seizures and a stress marker signalindicative of the occurrence of tissue stress; and detecting the partialepileptic seizure based upon the timewise correlation of two features,one feature being of each of the at least two body signals, wherein:

the feature of the cardiac signal is a patient's heart rate outside aninterictal reference value range;

the feature of the accelerometer signal is a movement outside aninterictal reference value range;

the feature of the respiratory signal is a respiration rate, tidalvolume, and/or minute volume outside an interictal reference valuerange;

the feature of the skin resistivity signal is a skin resistivity outsidean interictal reference value range;

the feature of the arterial blood gas signal is an arterial blood oxygencontent outside an interictal reference value range, an arterial carbondioxide content outside an interictal reference value range, or both;

the feature of the blood pH signal is a blood pH outside an interictalreference value range;

the feature of the sound signal is a change in the patient's oralutterances or vocalizations outside an interictal reference value range;

and the feature of the stress marker signal is an increase in thepatient's endocrine or stress markers above an interictal referencevalue.

Partial seizures can be distinguished from generalized seizures. Partialseizures that evolve into secondarily generalized seizures can also bedistinguished from primarily generalized seizures. Also, within theclass of partial seizures, simple partial seizures can be distinguishedfrom complex partial seizures.

In a further embodiment, this method further comprises classifying thepartial epileptic seizure as a complex partial seizure if a feature ofthe awareness signal timewise correlated with the at least one bodysignals is a decrease in the patient's awareness or other cognitivefunctions below an interictal reference value, and as a simple partialseizure if the patient's awareness or other cognitive function remain ator above an inter-ictal reference a feature of the awareness s signaltimewise correlated with the at least two body signals.

Alternatively or in addition, in one embodiment, the method furthercomprises indicating the end of the partial epileptic seizure when atleast one of the signal features the respective body signal is outsidethe range of values for the ictal and interictal periods for thatpatient and within the range of postictal values;

Alternatively or in addition, in one embodiment, the method furthercomprises indicating the beginning of a post-ictal period based upon theappearance of at least one post-ictal feature of at least one said bodysignal, wherein:

the cardiac signal is a heart rate outside an ictal reference valuerange and within a range indicative of a post-ictal state for saidpatient;

the accelerometer signal is a movement velocity, amplitude, or number ofmovements per unit time outside an ictal reference range of values andwithin a range indicative of a post-ictal state for said patient;

the accelerometer signal is a movement pattern, trajectory, orinter-movement intervals outside an ictal reference value range andwithin a range indicative of a post-ictal state for said patient;

the respiratory signal is a respiration rate outside the ictal range ofvalues for that patient and within a post-ictal range for said patient;

the responsiveness signal is a change in the patient's unawareness orcognitive dysfunction to a value outside both of an ictal range and aninterictal range; and

the awareness signal is a change in the patient's awareness to a valueoutside both an ictal range and an interictal range.

In a further embodiment, the method further comprises indicating the endof the post-ictal period when each of the features from the respectivebody signal returns to the range of values associated with theinterictal period.

Alternatively or in addition, in one embodiment, the method furthercomprises indicating the beginning of the inter-ictal period based uponthe appearance of at least one inter-ictal feature of at least two saidbody signal, wherein:

the inter-ictal feature of the cardiac signal is a return of thepatient's heart rate values to inter-ictal reference values and outsidea range indicative of a post-ictal and ictal state for said patient;

the inter-ictal feature of the accelerometer signal is a return of thepatient's movement velocities, amplitudes, or number of movements perunit time to the inter-ictal value range for that patient and outsidethe values or patterns indicative of a post-ictal and ictal states;

the inter-ictal feature of the accelerometer signal is a return of themovement patterns or trajectories to those present in the inter-ictalperiod for that patient and different from those present during thepost-ictal and ictal states;

the inter-ictal feature of the respiratory signal is return of therespiratory frequency to inter-ictal values for that patient and outsidethose indicative of post-ictal and ictal states;

the inter-ictal feature of the responsiveness signal is a return of thepatient's responsiveness to a range of values seen in the inter-ictalstate for that patient and outside a range of values indicative ofpost-ictal and/or ictal states

the inter-ictal feature of the awareness signal is a return of thepatient's awareness to a range of values seen in the inter-ictal statefor the patient and outside a range of values indicative of post-ictaland/or ictal states.

Regardless of how an epileptic event is detected, in some embodiments, aresponsive action may be taken selected from warning, logging the timeof an epileptic event, computing and storing one or more seizureseverity indices, or delivering a therapy to prevent, abate or lessenthe severity of the ictal or postictal states. Further responsiveactions such as warning, logging and treatment may be taken if the ictalor postictal states severity exceeds for example the 90th percentilevalues for a patient.

A warning may be given as, for example, a warning tone or lightimplemented by a medical device or a device adapted to receiveindications of the seizure; as an automated email, text message,telephone call, or video message sent from a medical device or a unit incommunication with a medical device to the patient's cellular telephone,PDA, computer, television, 911 or another emergency contact number forparamedic/EMT services, etc. Such a warning may allow the patient or hisor her caregivers to take measures protective of patient's well-beingand those of others, e.g., pulling out of traffic and turning off a car,when the patient is driving; stopping the use of machinery, contactinganother adult if the patient is providing childcare, removing thepatient from a swimming pool or bathtub, lying down or sitting if thepatient is standing, etc.

The time may be logged by receiving an indication of the current timeand associating the indication of the current time with an indication ofthe epileptic event.

Seizure severity indices may be calculated and stored by appropriatetechniques and apparatus.

A seizure may be treated by appropriate techniques, such as thosediscussed below. The treatment may be one or more treatments known inthe art. In one embodiment, the treatment comprises at least one ofapplying an electrical signal to a neural structure of a patient;delivering a drug to a patient; or cooling a neural structure of apatient. When the treatment comprises applying an electrical signal to aportion of a neural structure of a patient, the neural structure may beat least one of a portion of a brain structure of the patient, a portionof a cranial nerve of a patient, a portion of a spinal cord of apatient, a portion of a sympathetic nerve structure of the patient, aportion of a parasympathetic nerve structure of the patient, and/or aportion of a peripheral nerve of the patient.

Though not intended to be bound by theory, in certain circumstances, anepileptic event may be identified at a time before event onset would bedetermined by electroencephalography, observation by a physician orknowledgeable layman, or both. The time before onset may range from afew seconds up to a few minutes. As such, certain embodiments of themethod may be considered to yield a prediction of an epileptic event. Itshould be noted that the prediction may sometimes be a false positive.However, depending on a physician's judgment, his or her understandingof the devices in use, and the patient's condition, a certain amount offalse positives may be tolerable.

Even if no prediction is made, i.e., the methods of various embodimentsof this invention are capable of identifying an epileptic event at orafter the time of electrographic onset, such information may be usefulfor identifying an epileptic event without the need for EEG monitoring,implanted sensors, or clinical observation, and with a highersignal-to-noise ratio than EEG monitoring using scalp electrodes. Eventhough scalp recordings are the most common modality for seizuredetection, this modality has low sensitivity (e.g., a large number ofepileptic seizures are not accompanied by electrical changes at thescalp), low specificity (e.g., muscle and movement artifacts mayresemble electrical seizure activity at the scalp), and also may havelong latency between the emergence of epileptic activity in certainbrain regions and the appearance, if any, of electrical activity at thescalp.

EEG, implanted sensors, and clinical monitoring or observation oftenhave low signal-to-noise ratios, sensitivity and specificity. Thesemodalities are often disruptive of the patient's normal life, and cangenerally only be used for limited time periods due to the risk ofinfection and other injury. These undesirable limitations can generallybe avoided in some embodiments of the present method.

Also, the method can make use of multiple modalities of data, thusproviding a method of identifying epileptic seizures that may providegreater selectivity, sensitivity, and/or specificity. The information isalso useful for identifying an epileptic event with greater accuracythan is generally exhibited by seizure diaries. Surprisingly, seizurediaries kept by the patient are often highly inaccurate with numerousfalse negatives.

Although not limited to the following, an exemplary system capable ofimplementing embodiments of the present invention is described below.FIG. 1 depicts a stylized implantable medical system (IMD) 100 forimplementing one or more embodiments of the present invention. Anelectrical signal generator 110 is provided, having a main body 112comprising a case or shell with a header 116 for connecting to aninsulated, electrically conductive lead assembly 122. The generator 110is implanted in the patient's chest in a pocket or cavity formed by theimplanting surgeon just below the skin (indicated by a dotted line 145),similar to the implantation procedure for a pacemaker pulse generator.

A nerve electrode assembly 125, preferably comprising a plurality ofelectrodes having at least an electrode pair, is conductively connectedto the distal end of the lead assembly 122, which preferably comprises aplurality of lead wires (e.g., one wire for each electrode). Eachelectrode in the electrode assembly 125 may operate independently oralternatively, may operate in conjunction with the other electrodes. Inone embodiment, the electrode assembly 125 comprises at least a cathodeand an anode. In another embodiment, the electrode assembly comprisesone or more unipolar electrodes.

Lead assembly 122 is attached at its proximal end to connectors on theheader 116 of generator 110. The electrode assembly 125 may besurgically coupled to the vagus nerve 127 in the patient's neck or atanother location, e.g., near the patient's diaphragm or at theesophagus/stomach junction. Other (or additional) cranial nerves such asthe trigeminal and/or glossopharyngeal nerves may also be used todeliver the electrical signal in particular alternative embodiments. Inone embodiment, the electrode assembly 125 comprises a bipolarstimulating electrode pair 126, 128 (i.e., a cathode and an anode).Suitable electrode assemblies are available from Cyberonics, Inc.,Houston, Tex., USA as the Model 302 electrode assembly. However, personsof skill in the art will appreciate that many electrode designs could beused in the present invention. In one embodiment, the two electrodes arewrapped about the vagus nerve, and the electrode assembly 125 may besecured to the vagus nerve 127 by a spiral anchoring tether 130 such asthat disclosed in U.S. Pat. No. 4,979,511 issued Dec. 25, 1990 to ReeseS. Terry, Jr. Lead assembly 122 may be secured, while retaining theability to flex with movement of the chest and neck, by a sutureconnection to nearby tissue (not shown).

In alternative embodiments, the electrode assembly 125 may comprisetemperature sensing elements, blood pressure sensing elements, and/orheart rate sensor elements. Other sensors for other body parameters mayalso be employed. Both passive and active stimulation may be combined ordelivered by a single IMD according to the present invention. Either orboth modes may be appropriate to treat a specific patient underobservation.

The electrical pulse generator 110 may be programmed with an externaldevice (ED) such as computer 150 using programming software known in theart. A programming wand 155 may be coupled to the computer 150 as partof the ED to facilitate radio frequency (RF) communication between thecomputer 150 and the pulse generator 110. The programming wand 155 andcomputer 150 permit non-invasive communication with the generator 110after the latter is implanted. In systems where the computer 150 usesone or more channels in the Medical Implant Communications Service(MICS) bandwidths, the programming wand 155 may be omitted to permitmore convenient communication directly between the computer 150 and thepulse generator 110.

Turning now to FIG. 2, a block diagram depiction of a medical device 200is provided, in accordance with one illustrative embodiment of thepresent invention. In some embodiments, the medical device 200 may beimplantable (such as implantable electrical signal generator 110 fromFIG. 1), while in other embodiments the medical device 200 may becompletely external to the body of the patient.

The medical device 200 may comprise a controller 210 capable ofcontrolling various aspects of the operation of the medical device 200.The controller 210 is capable of receiving internal data or externaldata, and in one embodiment, is capable of causing a stimulation unit(not shown) to generate and deliver an electrical signal, a drug,cooling, or two or more thereof to one or more target tissues of thepatient's body for treating a medical condition. For example, thecontroller 210 may receive manual instructions from an operatorexternally, or may cause an electrical signal to be generated anddelivered based on internal calculations and programming. In otherembodiments, the medical device 200 does not comprise a stimulationunit. In either embodiment, the controller 210 is capable of affectingsubstantially all functions of the medical device 200.

The controller 210 may comprise various components, such as a processor215, a memory 217, etc. The processor 215 may comprise one or moremicrocontrollers, microprocessors, etc., capable of performing variousexecutions of software components. The memory 217 may comprise variousmemory portions where a number of types of data (e.g., internal data,external data instructions, software codes, status data, diagnosticdata, etc.) may be stored. The memory 217 may comprise one or more ofrandom access memory (RAM), dynamic random access memory (DRAM),electrically erasable programmable read-only memory (EEPROM), flashmemory, etc.

The medical device 200 may also comprise a power supply 230. The powersupply 230 may comprise a battery, voltage regulators, capacitors, etc.,to provide power for the operation of the medical device 200, includingdelivering the therapeutic electrical signal. The power supply 230comprises a power source that in some embodiments may be rechargeable.In other embodiments, a non-rechargeable power source may be used. Thepower supply 230 provides power for the operation of the medical device200, including electronic operations and the electrical signalgeneration and delivery functions. The power supply 230 may comprise alithium/thionyl chloride cell or a lithium/carbon monofluoride (LiCFx)cell if the medical device 200 is implantable, or may compriseconventional watch or 9V batteries for external (i.e., non-implantable)embodiments. Other battery types known in the art of medical devices mayalso be used.

The medical device 200 may also comprise a communication unit 260capable of facilitating communications between the medical device 200and various devices. In particular, the communication unit 260 iscapable of providing transmission and reception of electronic signals toand from a monitoring unit 270, such as a handheld computer or PDA thatcan communicate with the medical device 200 wirelessly or by cable. Thecommunication unit 260 may include hardware, software, firmware, or anycombination thereof.

The medical device 200 may also comprise one or more sensor(s) 212coupled via sensor lead(s) 211 to the medical device 200. The sensor(s)212 are capable of receiving signals related to a physiologicalparameter, such as the patient's heart beat, blood pressure, and/ortemperature, and delivering the signals to the medical device 200. Thesensor 212 may also be capable of detecting kinetic signal associatedwith a patient's movement. The sensor 212, in one embodiment, may be anaccelerometer. The sensor 212, in another embodiment, may be aninclinometer. In another embodiment, the sensor 212 may be an actigraph.In one embodiment, the sensor(s) 212 may be the same as implantedelectrode(s) 126, 128 (FIG. 1). In other embodiments, the sensor(s) 212are external structures that may be placed on the patient's skin, suchas over the patient's heart or elsewhere on the patient's torso. Thesensor 212, in one embodiment is a multimodal signal sensor capable ofdetecting various autonomic and neurologic signals, including kineticsignals associated with the patient's movement.

In one embodiment, the medical device 200 may comprise a autonomicsignal module 265 that is capable of collecting autonomic data, e.g.,cardiac data comprising fiducial time markers of each of a plurality ofheart beats. The autonomic signal module 265 may also process orcondition the autonomic data. The autonomic data may be provided by thesensor(s) 212. The autonomic signal module 265 may be capable ofperforming any necessary or suitable amplifying, filtering, andperforming analog-to-digital (A/D) conversions to prepare the signalsfor downstream processing. The autonomic data module 265, in oneembodiment, may comprise software module(s) that are capable ofperforming various interface functions, filtering functions, etc. Inanother embodiment, the autonomic signal module 265 may comprisehardware circuitry that is capable of performing these functions. In yetanother embodiment, the autonomic signal module 265 may comprisehardware, firmware, software and/or any combination thereof. A moredetailed illustration of the autonomic signal module 265 is provided inFIG. 3A and accompanying description below.

The autonomic signal module 265 is capable of collecting autonomic dataand providing the collected autonomic data to a detection module 285.

In one embodiment, the medical device 200 may comprise a neurologicalsignal module 275 that is capable of collecting neurologic data, e.g.,kinetic signals indicative of the patient's movement. The neurologicalsignal module 275 may also process or condition the neurologic data. Theneurologic data may be provided by the sensor(s) 212. The neurologicalsignal module 275 may be capable of performing any necessary or suitableamplifying, filtering, and performing analog-to-digital (A/D)conversions to prepare the signals for downstream processing. Theneurological signal module 275, in one embodiment, may comprise softwaremodule(s) that are capable of performing various interface functions,filtering functions, etc. In another embodiment, the neurological signalmodule 275 may comprise hardware circuitry that is capable of performingthese functions. In yet another embodiment, the neurological signalmodule 275 may comprise hardware, firmware, software and/or anycombination thereof. Further description of the neurologic signal module275 is provided in FIG. 3B and accompanying description below.

The neurological signal module 275 is capable of collecting autonomicdata and providing the collected autonomic data to a detection module285.

The detection module 285 is capable of detecting an epileptic eventbased upon an autonomic signal provided by autonomic signal module 265and neurological signal module 275. The detection module 285 canimplement one or more algorithms using the autonomic data and neurologicdata in any particular order, weighting, etc. The detection module 285may comprise software module(s) that are capable of performing variousinterface functions, filtering functions, etc. In another embodiment,the detection module 285 may comprise hardware circuitry that is capableof performing these functions. In yet another embodiment, the detectionmodule 285 may comprise hardware, firmware, software and/or anycombination thereof. Further description of the detection module 285 isprovided in FIG. 3C and accompanying description below.

In addition to components of the medical device 200 described above, amedical device system may comprise a storage unit to store an indicationof at least one of seizure or an increased risk of a seizure. Thestorage unit may be the memory 217 of the medical device 200, anotherstorage unit of the medical device 200, or an external database, such asa local database unit 255 or a remote database unit 250. The medicaldevice 200 may communicate the indication via the communications unit260. Alternatively or in addition to an external database, the medicaldevice 200 may be adapted to communicate the indication to at least oneof a patient, a caregiver, or a healthcare provider.

In various embodiments, one or more of the units or modules describedabove may be located in a monitoring unit 270 or a remote device 292,with communications between that unit or module and a unit or modulelocated in the medical device 200 taking place via communication unit260. For example, in one embodiment, one or more of the autonomic signalmodule 265, the neurologic signal module 275, or the detection module285 may be external to the medical device 200, e.g., in a monitoringunit 270. Locating one or more of the autonomic signal module 265, theneurologic signal module 275, or the detection module 285 outside themedical device 200 may be advantageous if the calculation(s) is/arecomputationally intensive, in order to reduce energy expenditure andheat generation in the medical device 200 or to expedite calculation.

The monitoring unit 270 may be a device that is capable of transmittingand receiving data to and from the medical device 200. In oneembodiment, the monitoring unit 270 is a computer system capable ofexecuting a data-acquisition program. The monitoring unit 270 may becontrolled by a healthcare provider, such as a physician, at a basestation in, for example, a doctor's office. In alternative embodiments,the monitoring unit 270 may be controlled by a patient in a systemproviding less interactive communication with the medical device 200than another monitoring unit 270 controlled by a healthcare provider.Whether controlled by the patient or by a healthcare provider, themonitoring unit 270 may be a computer, preferably a handheld computer orPDA, but may alternatively comprise any other device that is capable ofelectronic communications and programming, e.g., hand-held computersystem, a PC computer system, a laptop computer system, a server, apersonal digital assistant (PDA), an Apple-based computer system, acellular telephone, etc. The monitoring unit 270 may download variousparameters and program software into the medical device 200 forprogramming the operation of the medical device, and may also receiveand upload various status conditions and other data from the medicaldevice 200. Communications between the monitoring unit 270 and thecommunication unit 260 in the medical device 200 may occur via awireless or other type of communication, represented generally by line277 in FIG. 2. This may occur using, e.g., wand 155 (FIG. 1) tocommunicate by RF energy with an implantable signal generator 110.Alternatively, the wand may be omitted in some systems, e.g., systems inwhich the MD 200 is non-implantable, or implantable systems in whichmonitoring unit 270 and MD 200 operate in the MICS bandwidths.

In one embodiment, the monitoring unit 270 may comprise a local databaseunit 255. Optionally or alternatively, the monitoring unit 270 may alsobe coupled to a database unit 250, which may be separate from monitoringunit 270 (e.g., a centralized database wirelessly linked to a handheldmonitoring unit 270). The database unit 250 and/or the local databaseunit 255 are capable of storing various patient data. These data maycomprise patient parameter data acquired from a patient's body, therapyparameter data, seizure severity data, and/or therapeutic efficacy data.The database unit 250 and/or the local database unit 255 may comprisedata for a plurality of patients, and may be organized and stored in avariety of manners, such as in date format, severity of disease format,etc. The database unit 250 and/or the local database unit 255 may berelational databases in one embodiment. A physician may perform variouspatient management functions (e.g., programming parameters for aresponsive therapy and/or setting references for one or more detectionparameters) using the monitoring unit 270, which may include obtainingand/or analyzing data from the medical device 200 and/or data from thedatabase unit 250 and/or the local database unit 255. The database unit250 and/or the local database unit 255 may store various patient data.

One or more of the blocks illustrated in the block diagram of themedical device 200 in FIG. 2 may comprise hardware units, softwareunits, firmware units, or any combination thereof. Additionally, one ormore blocks illustrated in FIG. 2 may be combined with other blocks,which may represent circuit hardware units, software algorithms, etc.Additionally, any number of the circuitry or software units associatedwith the various blocks illustrated in FIG. 2 may be combined into aprogrammable device, such as a field programmable gate array, an ASICdevice, etc.

Turning to FIG. 3A, an autonomic signal module 265 is shown in moredetail. The autonomic signal module 265 can comprise a cardiovascularsignal unit 312 capable of providing at least one cardiovascular signal.Alternatively or in addition, the autonomic signal module 265 cancomprise a respiratory signal unit 314 capable of providing at least onerespiratory signal. Alternatively or in addition, the autonomic signalmodule 265 can comprise a blood parameter signal unit 323 capable ofproviding at least one blood parameter signal (e.g., blood glucose,blood pH, blood gas, etc). Alternatively or in addition, the autonomicsignal module 265 can comprise a temperature signal unit 316 capable ofproviding at least one temperature signal. Alternatively or in addition,the autonomic signal module 265 can comprise an optic signal unit 318capable of providing at least one optic signal. Alternatively or inaddition, the autonomic signal module 265 can comprise a chemical signalunit 320 capable of providing at least one body chemical signal.Alternatively or in addition, the autonomic signal module 265 cancomprise a hormone signal unit 322 capable of providing at least onehormone signal. Alternatively or in addition, the autonomic signalmodule 265 can comprise one or more other autonomic signal unit(s) 324,such as a skin resistance signal unit.

The autonomic signal module 265 can also comprise an autonomic signalprocessing unit 330. The autonomic signal processing unit 330 canperform any filtering, noise reduction, amplification, or otherappropriate processing of the data received by the signal units 312-324desired by the person of ordinary skill in the art prior to calculationof the autonomic signal.

The autonomic signal module 265 can also comprise an autonomic signalcalculation unit 340. The autonomic signal calculation unit 340 cancalculate an autonomic signal from the data passed by the autonomicsignal processing unit 330. A calculated autonomic signal herein refersto any signal derivable from the provided signals, with or withoutprocessing by the autonomic signal processing unit 330.

For example, the autonomic signal calculation unit 340 may calculate theheart rate, a change in the heart rate, the speed of change in heartrate, blood pressure, heart sounds, heart rhythm, heartbeat morphologyat various scales (see, e.g., U.S. Ser. No. 12/884,051, filed Sep. 16,2010, and U.S. Ser. No. 12/886,419, filed Sep. 20, 2010, which arehereby incorporated herein by reference), or the shape of the deflectionof the thoracic wall as the heart apex beats against it, among others,from cardiovascular data received by cardiovascular signal unit 312.

For another example, the autonomic signal calculation unit 340 maycalculate the respiration (breath) rate, respiration pattern, airflowvelocity, respiration amplitude (tidal volume, minute volume), arterialgas concentrations such as end-tidal carbon dioxide, among others, fromrespiratory data received by respiratory signal unit 314.

For still another example, the autonomic signal calculation unit 340 maycalculate a change in the skin temperature or skin electrical resistanceof a part of the patient's face or a change in the core temperature ofthe patient, from temperature data received by temperature signal unit316.

Turning to FIG. 3B, an exemplary embodiment of a neurologic signalmodule 275 is shown. The neurologic signal module 275 can comprise atleast one of a neuro-electrical signal unit 3012 capable of providing atleast one neuro-electrical signal; a neuro-chemical signal unit 3014capable of providing at least one neuro-chemical signal; aneuro-electrochemical signal unit 3016 capable of providing at least oneneuro-electrochemical signal; a kinetic signal unit 3018 capable ofproviding at least one kinetic signal; or a cognitive signal unit 3020capable of providing at least one cognitive signal. The cognitive signalunit 3020 may be a component of a remote device.

In one embodiment, the cognitive signal unit comprises at least one of acognitive aptitude determination unit 3020 a capable of processing atleast one cognitive aptitude indication; an attention aptitudedetermination unit 3020 b capable of processing at least one attentionaptitude indication; a memory aptitude determination unit 3020 c capableof processing at least one memory indication; a language aptitudedetermination unit 3020 d capable of processing at least one languageindication; a visual/spatial aptitude determination unit 3020 e capableof processing at least one visual/spatial indication; one or more otherneurologic factor determination unit(s) 3020 g; or a responsivenessdetermination unit 3020 h.

The neurologic signal module 275 can also comprise a neurologic signalprocessing unit 3030. The neurologic signal processing unit 3030 canperform any filtering, noise reduction, amplification, or otherappropriate processing of the data received by the signal units3012-3020 desired by the person of ordinary skill in the art prior tocalculation of the neurologic signal.

The neurologic signal module 275 can also comprise a neurologic signalcalculation unit 3040. The neurologic signal calculation unit 3040 cancalculate a neurologic signal from the data passed by the neurologicsignal processing unit 3030. A calculated neurologic signal hereinrefers to any signal derivable from the provided signals.

For example, the neurologic signal calculation unit 3040 may calculate abrain activity, such as those determinable from signals yielded by anEEG, ECoG, or depth electrodes (i.e., a deep brain electrode), asreceived by neuro-electrical signal unit 3012, neuro-chemical signalunit 3014, and/or neuro-electrochemical signal unit 3016 and,optionally, further processed by neurologic data processing unit 3030.

A calculated signal regarding brain activity can also be calculatedusing other neurological signals. For example, spikes in neurons oraxons in the brain and spinal cord including central structures andpathways with autonomic control or modulatory capabilities, cranialnerves (e.g., vagus nerve), autonomic ganglia or nerves and peripheralnerves can be sensed and signals provided. Neural imaging or brainimaging signals may be provided, including, for example: FunctionalMagnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG),Positron Emission Tomography (PET), Event-Related Optical Signal (EROS),and Diffuse Optical Imaging (DOI)). Other imaging techniques such asvoltage-sensitive dyes, ultrasound, infra-red, near infra-red and otherforms of thermography may also provide signals from which a brainactivity can be calculated.

For another example, the neurologic signal calculation unit 3040 maycalculate a body kinetic signal, such as the body's (or of a portionthereof such as the head, an arm, or a leg) acceleration; direction;position; smoothness, amplitude, force of movements and number ofmovements per unit time, and whether there are extraneous or abnormalbody oscillations during resting conditions or movement. The signal maybe provided by electromyography, a mechanogram, an accelerometer, anactigraph, and/or an inclinometer, as received by kinetic capabilitydetermination unit 3018, and, optionally, further processed byneurologic data processing unit 3030.

Kinetic signals provide insight into the functional state of the nervoussystem and are thus classified as a neurologic signal. The ability toperform movements: a) in any direction; b) do it smoothly and withprecision so that for example, a target (e.g. putting a key into itshole) may be met in the first attempt or handwriting is legible; c)changing direction to avoid colliding with an object interposed on itspath to a target and re-adjusting the trajectory to reach the originaltarget; and d) with adaptive force and discriminations so to be able topick a penny off a flat surface and also lift heavy objects. Theacceleration and velocity speed, direction and smoothness may bequantified using tools such as 3-D accelerometers among others.

Turning to FIG. 3C, a block diagram of detection module 285 is depicted.The detection module 285 comprises a calculated signal receiving module3110 capable of receiving data indicative of a calculated signal fromone or more of the autonomic signal module 265 and the neurologic signalmodule 275 or a memory 217 storing prior outputs of such a module, andepilepsy event determination module 3120 capable of determining from thereceived data the occurrence of an epileptic event, e.g., a seizure. Theepilepsy event determination module 3120 may implement any appropriatealgorithms for determining an epilepsy event from autonomic signals andneurologic signals, e.g., from cardiac data and kinetic data, such asthose referred to above.

In the embodiment shown in FIG. 3C, the detection module 285 furthercomprises an epilepsy event quantification module 3130 capable ofquantifying from the received data one or more quantifiablecharacteristics of an epileptic event, e.g., a seizure. Exemplaryquantifiable characteristics include, but are not limited to, eventduration, duration of stages of the event (e.g., preictal, ictal, and/orpostictal stages), values and/or ranges thereof of one or more bodysignals (e.g., a peak heart rate, a time series of heart rate, etc.),among others.

In the embodiment shown in FIG. 3C, the detection module 285 alsocomprises an epilepsy event classification module 3140 capable ofclassifying an epileptic event, e.g., a seizure, e.g., as a partialseizure, a generalized seizure, or an absence seizure; as a simplepartial or complex partial seizure; as a primarily generalized seizureor a secondarily generalized seizure, etc. This module may be also usedto classify events as epileptic or non-epileptic (e.g., pseudo-seizures,psychogenic seizures, etc.) Although FIG. 3C shows both an epilepsyevent quantification module 3130 and an epilepsy event classificationmodule 3140, in other embodiments, either or both of modules 3130-3140may be omitted.

The detection module 285 may send the output of the epilepsy eventdetermination module 3120 to one or more other modules of the medicaldevice 200 and/or one or more external units. The one or more othermodules may then store the output, report the output to the patient, aphysician, and/or a caregiver; warn the patient or a caregiver of anepileptic event, etc.

The medical device system of one embodiment of the present inventionprovides for software module(s) that are capable of acquiring, storing,and processing various forms of data, such as patient data/parameters(e.g., physiological data, side-effects data, heart rate data, breathingrate data, brain-activity parameters, disease progression or regressiondata, quality of life data, etc.) and therapy parameter data. Therapyparameters may include, but are not limited to, electrical signalparameters (e.g., frequency, pulse width, wave shape, polarity, geometryof electrical fields, on-time, off-time, etc.) that define therapeuticelectrical signals delivered by the medical device in response to thedetection of the seizure, medication type, dose, or other parameters,and/or any other therapeutic treatment parameter.

In one embodiment, the medical device 200 or an external unit 270 mayalso be capable of detecting a manual input from the patient. The manualinput may include a magnetic signal input, a tap input, a button, dial,or switch input, a touchscreen input, a wireless data input to themedical device 200, etc. The manual input may be used to allow captureof the patient's subjective assessment of his or her epileptic events.For example, the medical device 200 may comprise one or more physical orvirtual (e.g., touchscreen-implemented) buttons accessible to thepatient's fingers or a caregiver's, through which the patient orcaregiver may indicate he or she is having an epileptic event or is nothaving an epileptic event. Alternatively or in addition, the manualinput may be used to gauge the patient's responsiveness.

The above methods may be performed by a computer readable programstorage device encoded with instructions that, when executed by acomputer, perform the method described herein.

All of the methods and apparatuses disclosed and claimed herein may bemade and executed without undue experimentation in light of the presentdisclosure. While the methods and apparatus of this invention have beendescribed in terms of particular embodiments, it will be apparent tothose skilled in the art that variations may be applied to the methodsand apparatus and in the steps, or in the sequence of steps, of themethod described herein without departing from the concept, spirit, andscope of the invention, as defined by the appended claims. It should beespecially apparent that the principles of the invention may be appliedto selected cranial nerves other than, or in addition to, the vagusnerve to achieve particular results in treating patients havingepilepsy, depression, or other medical conditions.

In certain embodiments, the present invention relates to one or more ofthe following numbered paragraphs:

1. A medical device (implantable or non-implantable) for detecting,quantifying, and/or classifiying an epileptic event based upon anautonomic signal and a neurologic signal of a patient, comprising:a detection, quantification, and/or classification module(s) forreceiving an autonomic signal indicative of the patient's autonomicactivity and for receiving a neurologic signal indicative of thepatient's neurological activity;a processing element for determining whether an epileptic event hasoccurred, a quantifiable characteristic of an epileptic event, and/or aclass of an epileptic event, based upon the autonomic signal and theneurologic signal.2. The implantable medical device of numbered paragraph 1, wherein theautonomic signal is selected from the group consisting of a cardiacsignal, a respiratory signal, a skin resistivity signal, a pupillarysignal, a blood signal, and two or more thereof3. The implantable medical device of numbered paragraph 1, wherein theneurologic signal is selected from the group consisting of a brainsignal, a kinetic signal, and two or more thereof4. The implantable medical device of numbered paragraph 1, wherein theprocessing element is further capable of determining whether anepileptic event has occurred, quantifying an epileptic event, orclassifying an epileptic event by: identifying a putative epilepticevent, a quantifiable characteristic of an epileptic event, and/or aclass of an epileptic event based on one of the autonomic signal and theneurologic signal; and validating the identifying based on the other ofthe autonomic signal and the neurologic signal.101. A method for detecting a generalized tonic-clonic epileptic seizurebased upon two or more of a patient's body signals, comprising:providing at least two body signals selected from the group consistingof a cardiac signal indicative of the patient's heart beats; anaccelerometer signal indicative of the patient's movement; aninclinometer signal indicative of the patient's body position; anactigraph signal indicative of the patient's movement, body position, orboth; a respiratory signal indicative of the patient's respiration; askin resistivity signal indicative of the patient's skin resistivity; anblood gas signal indicative of the patient's blood oxygen content,carbon dioxide content, or both; a blood pH signal indicative of thepatient's blood pH; an isometric force signal indicative of thepatient's muscle activity; a sound signal indicative of the patient'soral utterances or vocalizations; an ocular signal indicative of thepatient's eye movement; a responsiveness signal indicative of thepatient's responsiveness; an awareness signal indicative of thepatient's awareness; an eye activity signal indicative of the patient'seye activity; and a stress marker signal indicative of at least onestress marker of the patient; anddetecting the generalized tonic-clonic epileptic seizure based upon thetimewise correlation of two features, one feature being of each of theat least two body signals, wherein:the feature of the cardiac signal is an increase in the patient's heartrate above an interictal reference value; the feature of theaccelerometer signal is an increase in the patient's movement above aninterictal reference value followed by a decrease in the patient'smovement below an interictal reference value;the feature of the inclinometer signal is a change of the patient's bodyposition indicative of either loss of or increase in postural tone,resulting in a fall;the feature of the actigraph signal is an increase in the patient'smovement above an interictal reference value followed by a decrease inthe patient's movement below an interictal reference value, or a changeof the patient's body position indicative of a change in body postureincluding but not limited to a fall;the feature of the respiratory signal is a respiration rate outside aninterictal reference value range;the feature of the skin resistivity signal is a change in the patient'sskin resistivity outside an interictal reference value;the feature of the blood gas signal is a decrease in the patient's bloodoxygen content below an interictal reference value, an increase incarbon dioxide content above an interictal reference value, or both;the feature of the blood pH signal is a decrease in the patient's bloodpH below an interictal reference value;the feature of the isometric force signal is an increase in thepatient's muscle activity above an interictal reference value;the feature of the sound signal is an increase in the patient's oralutterances or vocalizations indicative of an epileptic cry;the feature of the ocular signal is a forced eye deviation;the feature of the responsiveness signal is a decrease in the patient'sresponsiveness below an interictal reference value;the feature of the awareness signal is a decrease in the patient'sawareness below an interictal reference value;the feature of the eye signal is a change in one or more of blinkingrate, blinking amplitude, and inter-blinking interval outside aninterictal reference value range; and the feature of the stress markersignal is an increase in at least one stress marker of the patient abovean interictal reference value.102. The method of numbered paragraph 101, further comprising:indicating the end of the generalized tonic-clonic epileptic seizurewhen each of the features is outside the value or range of valuesassociated with the ictal state for the respective body signal.103. The method of numbered paragraph 101, further comprising:indicating the beginning of a post-ictal period based upon theappearance of at least one post-ictal feature of at least one said bodysignal, wherein:the post-ictal feature of the cardiac signal is a decrease in thepatient's heart rate below an ictal reference value;the post-ictal feature of the accelerometer signal is a decrease in thepatient's movement below an ictal reference value;the post-ictal feature of the respiratory signal is an increase in thepatient's respiration rate above an ictal reference value;the post-ictal feature of the responsiveness signal is an increase inthe patient's responsiveness above an ictal value and below aninter-ictal reference value; andthe post-ictal feature of the awareness signal is an increase in thepatient's awareness above an ictal value and below an inter-ictalreference value.104. The method of numbered paragraph 103, further comprising:indicating the end of the post-ictal period when each of the post-ictalfeatures is outside the range of values associated with the ictal andpost-ictal states.201. A computer readable program storage device encoded withinstructions that, when executed by a computer, performs a method fordetecting a partial epileptic seizure based upon two or more of apatient's body signals, comprising:providing at least two body signals selected from the group consistingof a cardiac signal indicative of the patient's heart beats; anaccelerometer signal indicative of the patient's movement; aninclinometer signal indicative of the patient's body position; anactigraph signal indicative of the patient's movement, body position, orboth; a respiratory signal indicative of the patient's respiration; askin resistivity signal indicative of the patient's skin resistivity; anblood gas signal indicative of the patient's blood oxygen content,carbon dioxide content, or both; a blood pH signal indicative of thepatient's blood pH; a sound signal indicative of the patient's oralutterances or vocalizations; a responsiveness signal indicative of thepatient's responsiveness; an awareness signal indicative of thepatient's awareness; an eye activity signal indicative of at least oneeye activity of a patient; and a stress marker signal indicative of atleast one stress marker of the patient; anddetecting the partial epileptic seizure based upon the timewisecorrelation of two features, one feature being of each of the at leasttwo body signals, wherein:the feature of the cardiac signal is a heart rate outside an interictalreference value range;the feature of the accelerometer signal is a movement outside aninterictal reference value range;the feature of the actigraph signal is a movement outside an interictalreference value range;the feature of the respiratory signal is a respiration rate outside aninterictal reference value range;the feature of the skin resistivity signal is a skin resistivity outsidean interictal reference value;the feature of the blood gas signal is a blood oxygen content outside aninterictal reference value range, a carbon dioxide content outside aninterictal reference value range, or both;the feature of the blood pH signal is a blood pH outside an interictalreference value range;the feature of the sound signal is an increase or a decrease in thepatient's oral utterances or vocalizations above an interictal referencevalue;the feature of the eye signal is a change in one or more of blinkingrate, blinking amplitude, and inter-blinking interval outside aninterictal reference value range;and the feature of the stress marker signal is a concentration of atleast one stress marker of the patient above an interictal referencevalue.202. The computer readable program storage device encoded withinstructions that, when executed by a computer, perform the method ofnumbered paragraph 201, wherein the method further comprises:classifying the partial epileptic seizure as a complex partial seizureif a feature of the signal timewise correlated with the at least twobody signals and/or a feature of the awareness signal timewisecorrelated with the at least two body signals is a decrease in thepatient's responsiveness below its reference value and/or awarenessbelow its reference value, and as a simple partial seizure if there isno decrease in the patient's responsiveness below its reference valueand no decrease in the patient's awareness below its reference value or,if there is a decrease in the patient's responsiveness but awarenessremains preserved.203. The computer readable program storage device encoded withinstructions that, when executed by a computer, perform the method ofnumbered paragraph 201, wherein the method further comprising:indicating the end of the partial epileptic seizure when each of thefeatures of the respective body signals is outside the range of valuesassociated with the ictal state for that body signal.204. The computer readable program storage device encoded withinstructions that, when executed by a computer, perform the method ofnumbered paragraph 201, wherein the method further comprising:indicating the beginning of a post-ictal period when each of therespective body signals is outside the range of values associated withthe ictal and inter-ictal states for that body signal, wherein:the cardiac signal outside the range of values associated with the ictalstate;the post-ictal feature of the accelerometer signal is a change in thepatient's movement outside the ictal range of values;the post-ictal feature of the respiratory signal is a change in thepatient's respiration to a value outside an ictal reference value;the post-ictal feature of the responsiveness signal is an increase inthe patient's responsiveness above an ictal reference value butremaining below and inter-ictal reference value; andthe post-ictal feature of the awareness signal is an increase in thepatient's awareness above an ictal reference value but remaining belowan inter-ictal reference value.205. The computer readable program storage device encoded withinstructions that, when executed by a computer, perform the method ofnumbered paragraph 204, wherein the method further comprises:indicating the end of the post-ictal period when each of the features isabsent from its respective body signal.301. A medical device system for detecting an epileptic event based uponmultimodal signals, comprising:a first sensor for detecting a first modal data and a second sensor fordetecting a second modal data relating to a patient's body, the firstmodal data comprising a neurologic signal and the second modal datacomprising an autonomic signal of the patient's body; andan implantable medical device (IMD) operatively coupled to the firstsensor and the second sensor; the IMD comprising:a neurologic signal module for receiving the neurologic signal;an autonomic signal module for receiving the autonomic signal; anda processing element for determining whether an epileptic event hasoccurred based upon the first and second modal data.401. A method for detecting, quantifying, and/or classifying anepileptic event based upon two or more of a patient's body signals,comprising:providing at least one body signal selected from the group consisting ofa cardiac signal indicative of the patient's heart beats; anaccelerometer signal indicative of the patient's movement; aninclinometer signal indicative of the patient's body position; anactigraph signal indicative of the patient's movement, body position, orboth; a respiratory signal indicative of the patient's respiration; askin resistivity signal indicative of the patient's skin resistivity; anblood gas signal indicative of the patient's blood oxygen content,carbon dioxide content, or both; a blood pH signal indicative of thepatient's blood pH; an isometric force signal indicative of thepatient's muscle activity; a sound signal indicative of the patient'soral utterances or vocalizations; an ocular signal indicative of thepatient's eye movement; a responsiveness signal indicative of thepatient's responsiveness; an awareness signal indicative of thepatient's awareness; and a stress marker signal indicative of at leastone stress marker of the patient;detecting a transition from a preictal time period to an ictal timeperiod using at least a first body signal;detecting a transition from a preictal time period to an ictal timeperiod using at least a second body signal;detecting a transition from an ictal time period to a postictal timeperiod using at least a third body signal; anddetecting a transition from a postictal time period to an interictaltime period using at least a fourth body signal.402. The method of numbered paragraph 401, wherein the first bodysignal, the second body signal, the third, body signal and the fourthbody signal are selected from at least two provided body signals.The particular embodiments disclosed above are illustrative only as theinvention may be modified and practiced in different but equivalentmanners apparent to those skilled in the art having the benefit of theteachings herein. Furthermore, no limitations are intended to thedetails of construction or design herein shown other than as describedin the claims below. It is, therefore, evident that the particularembodiments disclosed above may be altered or modified and all suchvariations are considered within the scope and spirit of the invention.Accordingly, the protection sought herein is as set forth in the claimsbelow.

1.-31. (canceled)
 32. A method for detecting an epileptic event basedupon a patient's cardiac signal and kinetic activity, comprising:providing a cardiac signal indicative of the patient's heart activity;determining at least one cardiac index from the cardiac signal, whereinsaid at least one cardiac index is selected from a heart rate and atleast one EKG morphology feature; providing a kinetic signal indicativeof body movement of the patient; detecting an epileptic event based on achange in the at least one cardiac index selected from: an increase inthe patient's heart rate; a decrease in the patient's heart rate; or achange in said at least one EKG morphology feature; and performing afurther action based on said kinetic signal, wherein said further actionis selected from confirming the detecting of the epileptic event; or notconfirming the detecting of the epileptic event.
 33. The method of claim32 further comprising: determining at least one kinetic index selectedfrom a body posture, a body position, a fall, a parameter derived froman accelerometer signal selected from a movement, a frequency per unittime, a time interval between movements, a movement direction in atleast one plane, an acceleration, a velocity, a force, or a change inone or more of the foregoing, and wherein performing said further actionis based on the at least one kinetic index.
 34. The method of claim 32further comprising determining a kinetic index selected from a cessationof movement or an increase in the patient's heart rate.
 35. The methodof claim 33 wherein performing said further action based on said kineticsignal is based upon the temporal relationship between the change in theat least one cardiac index and the kinetic signal.
 36. The method ofclaim 32 further comprising classifying said epileptic event based uponat least one cardiac index and at least one kinetic index.
 37. Themethod of claim 32, further comprising classifying said epileptic eventbased upon at least one of the patient's heart rate, heart rhythm, EKGmorphology, EKG wave intervals, an accelerometer signal, an inclinometersignal, a respiratory signal, a skin signal, a blood gases signal, ablood pH, an force, vocal sounds, an EMG signal, an oculogram signal, anEEG signal, an ECoG signal, a responsiveness of the patient, anawareness of the patient, or a stress marker.
 38. The method of claim 37wherein classifying said event comprises classifying the event as one ormore of: an unconfirmed seizure; a confirmed seizure; a non-epilepticseizure; a primarily generalized tonic-clonic seizure, a secondarilygeneralized tonic-clonic seizure; an absence seizure; a tonic seizure;an atonic seizure a partial seizure; a simple partial seizure; a complexpartial seizure; a simple partial seizure with secondary generalization;or a complex partial seizure with secondary generalization.
 39. Themethod of claim 32, further comprising providing, following saidconfirming the detecting of the epileptic event, at least one of aresponsiveness signal indicative of the patient's responsiveness and anawareness signal indicative of the patient's awareness, and classifyingthe epileptic event based upon the at least one of the responsivenesssignal and the awareness signal.
 40. A method for detecting an epilepticevent based upon a patient's cardiac signal and kinetic activity,comprising: providing a kinetic signal indicative of body movement ofthe patient; determining at least one kinetic index from said kineticsignal, wherein said at least one kinetic index is selected from a bodyposture, a body position, a fall, a parameter derived from anaccelerometer signal selected from a movement, a frequency per unittime, a time interval between movements, a movement direction in atleast one plane, an acceleration, a velocity, a force, or a change inone or more of the foregoing; providing a cardiac signal indicative ofthe patient's heart activity; detecting an epileptic event based on saidat least one kinetic index; and performing a further action based onsaid cardiac signal, wherein said further action is selected fromconfirming the detecting of the epileptic event; or not confirming thedetecting of the epileptic event.
 41. The method of claim 40 furthercomprising: determining at least one cardiac index selected from a heartrate, an EKG morphology feature, an EKG rhythm feature, or an EKG waveinterval feature, and wherein performing said further action is based onthe at least one cardiac index.
 42. The method of claim 41, furthercomprising classifying said epileptic event based upon at least one of acardiac index, a kinetic index, a respiratory index, a skin index, ablood gases signal, a blood pH, a force index, vocal sounds, ocularactivity, an EEG signal, an ECoG signal, an EMG signal, a responsivenessof the patient, an awareness of the patient, or a stress marker.
 43. Themethod of claim 42 wherein classifying said event comprises classifyingthe event as one or more of: an unconfirmed seizure; a confirmedseizure; a non-epileptic seizure; a primarily generalized tonic-clonicseizure, a secondarily generalized tonic-clonic seizure; an absenceseizure; a tonic seizure; an atonic seizure a partial seizure; a simplepartial seizure; a complex partial seizure; a simple partial seizurewith secondary generalization; or a complex partial seizure withsecondary generalization.
 44. The method of claim 40, further comprisingproviding, following said confirming the detecting of the epilepticevent, at least one of a responsiveness signal indicative of thepatient's responsiveness or an awareness signal indicative of thepatient's awareness, and classifying the epileptic event based upon theat least one of the responsiveness signal or the awareness signal.
 45. Amethod for detecting an epileptic event based upon a patient's cardiacsignal and kinetic activity, comprising: providing a cardiac signalindicative of the patient's heart activity; determining at least onecardiac index from the cardiac signal, wherein said at least one cardiacindex is selected from a heart rate, an EKG morphology feature, an EKGrhythm feature, or an EKG wave interval feature; characterizing the atleast one cardiac index as either indicative of an epileptic event ornot indicative of an epileptic event; providing a kinetic signalindicative of body movement and type of body movement of the patient;characterizing the kinetic signal as either indicative of an epilepticevent or not indicative of an epileptic event; and detecting anepileptic event only if both of the heart rate and the kinetic signalare indicative of an epileptic event.
 46. The method of claim 45 furthercomprising performing a further action in response to detecting anepileptic event, wherein said further action is selected from providinga therapy to the patient, providing a warning to one of a patient or acaregiver, or logging data relating to the epileptic event.
 47. Themethod of claim 45 further comprising classifying said epileptic eventbased upon at least one cardiac index and at least one kinetic index.48. The method of claim 47 wherein classifying said event comprisesclassifying the event as one or more of: an unconfirmed seizure; aconfirmed seizure; a non-epileptic seizure; a primarily generalizedtonic-clonic seizure, a secondarily generalized tonic-clonic seizure; anabsence seizure; a tonic seizure; an atonic seizure a partial seizure; asimple partial seizure; a complex partial seizure; a simple partialseizure with secondary generalization; a complex partial seizure withsecondary generalization.
 49. The method of claim 45, wherein thecardiac signal is provided by an electrocardiogram (EKG), and thekinetic signal is provided by an accelerometer, an inclinometer, anactigraph, an EMG, a mechanogram or two or more thereof.
 50. The methodof claim 45, further comprising providing an awareness signal indicativeof the patient's awareness following said detecting, and characterizingthe epileptic event based upon the awareness signal.
 51. A medicaldevice system for detecting an epileptic event based upon multimodalsignals, comprising: a first sensor for detecting a first modal data anda second sensor for detecting a second modal data relating to apatient's body, the first modal data comprising a kinetic signal and thesecond modal data comprising a cardiac signal of the patient's body; anda medical device (MD) coupled to the patient's body and operativelycoupled to the first sensor and the second sensor; the MD comprising: akinetic signal module for receiving the kinetic signal; a cardiac signalmodule for receiving the cardiac signal; a detection module fordetermining whether an epileptic event has occurred based on: 1)detecting an epileptic event using one of said kinetic signal module andsaid cardiac signal module, and 2) confirming or not confirming saidepileptic event using the other of said kinetic signal module and saidcardiac signal module,
 52. The medical device system of claim 51 furthercomprising an output module for providing a signal indicating at leastone of no seizure detection, a possible seizure detection, or aconfirmed seizure detection.